human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the emergence of number and syntax units in lstm language models"):
  Abstract: Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly take hierarchical structure into account. We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two ``number units''. Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure. We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.

Includes members:
  Member "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "indicatements that character language models learn english morpho syntactic units and regularities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "what do rnn language models learn about filler gap dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "distinct patterns of syntactic agreement errors in recurrent networks and humans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "do language models understand anything on the ability of lstms to understand negative polarity items":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "colorless green recurrent networks dream hierarchically":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "learning to generate reviews and discovering sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "targeted syntactic evaluation of language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised recurrent neural network grammars")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CL(Computation and Language)