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 "cooperative learning of disjoint syntax and semantics"):
  Abstract: There has been considerable attention devoted to models that learn to jointly infer an expression's syntactic structure and its semantics. Yet, \citet{NangiaB18} has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by \newcite{ChoiYL18} that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimization schemes. Our model does not require any linguistic structure for supervision and its recursive nature allows for out-of-domain generalization with little loss in performance. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference or Sentiment Analysis.

Includes members:
  Member "on tree based neural sentence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tree structured attention with hierarchical accumulation")
  Member "listops a diagnostic dataset for latent tree learning":
    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 "latent tree learning with differentiable parsers shift reduce parsing and chart parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "towards dynamic computation graphs via sparse latent structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction")
  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 "automatically composing representation transformations as a means for generalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards modular algorithm induction")

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)