As an NLP researcher, I see lots of NLP papers on top of AAAI. I think that the high citation count is due to

(i) people in NLP actually bothering to tell others why their approach is interesting

(ii) other people being interested in the same / a similar kind of thing [avoiding the discipline-of-one problem that niche AI applications would have] and

(iii) NLP having a reasonably developed "canon" about what counts as must-cite papers. This canon is heavily biased towards US work, and towards people who write decent explanations of what they do, but at least it makes sure that people know about the big problems and failed (or not-quite-failed-as-badly-as-the-others) solutions.

What you see in other conferences is that the "Best paper" awards get to (i) more theoretical papers which still have issues to solve before people can use the approach (nothing wrong with those!), in (ii) subfields that are currently "hot". Whereas the most-cited papers are (i) more obviously about things that a dedicated person could apply in practice, and (ii) in a subfield that is obscure at the time but will become more popular in the following years.