Map two data frames by common string elements from List column

I want to map two data frames if the string element from two columns match, The common column i have is string with comma separated. I tried map function by converting it to dictionary also. But it didn't worked.

df 
Text
[Temp,Temp2]
[Temp4,Temp7,Temp2]


ClusterDf
Label             Member
[Cluster1]    [Temp,Temp8]
[Cluster2]   [Temp4,Temp7]

I want output like

df 
Text                     Label  
[Temp,Temp2]             [Cluster1]  
[Temp4,Temp7,Temp2]      [Cluster2]

Create dictionary by ClusterDf and then add new column by map with next and iter if no match:

d = {v: a[0] for a, b in zip(ClusterDf['Label'], ClusterDf['Member']) for v in b}
print (d)
{'Temp': 'Cluster1', 'Temp8': 'Cluster1', 'Temp4': 'Cluster2', 'Temp7': 'Cluster2'}

df['Label'] = df['Text'].map(lambda x: next(iter(d[y] for y in x if y in d), 'no match'))
print (df)
                    Text     Label
0          [Temp, Temp2]  Cluster1
1  [Temp4, Temp7, Temp2]  Cluster2

If need list:

df['Label'] = df['Text'].map(lambda x: [next(iter(d[y] for y in x if y in d), 'no match')])
print (df)
                    Text       Label
0          [Temp, Temp2]  [Cluster1]
1  [Temp4, Temp7, Temp2]  [Cluster2]

If want all matching if exist:

df['Label'] = df['Text'].map(lambda x: [d[y] for y in x if y in d])
print (df)
                    Text                 Label
0          [Temp, Temp2]            [Cluster1]
1  [Temp4, Temp7, Temp2]  [Cluster2, Cluster2]

Thanks @jezrael, Third solution worked for me perfectly. Thanks a lot.
You made my day

df['Label'] = df['Text'].map(lambda x: [d[y] for y in x if y in d])