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 "finding syntactic representations in neural stacks"):
  Abstract: Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.

Includes members:
  Member "context free transductions with neural stacks":
    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 "straight to the tree constituency parsing with neural syntactic distance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "targeted syntactic evaluation of language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "grammar induction with neural language models an unusual replication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "neural language modeling by jointly learning syntax and lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "assessing the ability of lstms to learn syntax sensitive dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "learning to transduce with unbounded memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "encoding based memory modules for recurrent neural networks")
  Member "inferring algorithmic patterns with stack augmented recurrent nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")

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)