Topic modeling for short length sentences
A possible approach might be to use the most predictable and predictive word(s) in each cluster as its name(s). The following is inspired by Fisher's category utility used by the COBWEB algorithm.

An attribute is highly predictable in a cluster $C_l$ if most elements in that cluster have the same value $a$ for it, thus a word represented by attribute $A_i$ has a predictability of $P(A_i=a|C_l)$

An attribute is highly predictive for a cluster $C_l$ if knowing it's value $a$ implies you can say with high certainty to which cluster it belongs, expressed as $P(C_l|A_i=a)$

Assume now you process each label org.java.somepackage.validateLogin as a sentence: "org java somepackage validate Login" and apply one-hot encoding to all your sentences in the dataset. The occurrence of a word is now represented by a value of 1 for it's corresponding attribute.

The task of representing each cluster by a word can now be formulated as finding for each cluster $C_l$ the word represented by attribute $A_i$ that equals 1 and has the highest predictability and predictiveness, weighted by the total probability of this word being in a sentence.

$$ P(A_i=1|C_l)\times P(C_l|A_i=1) \times P(A_i=1)$$

Which can be calculated by counting occurrence of a word per cluster and cluster per word.