human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "experimenting with transitive verbs in a discocat"
  Abstract: Formal and distributional semantic models offer complementary benefits in modeling meaning. The categorical compositional distributional (DisCoCat) model of meaning of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) combines aspected of both to provide a general framework in which meanings of words, obtained distributionally, are composed using methods from the logical setting to form sentence meaning. Concrete consequences of this general abstract setting and applications to empirical data are under active study (Grefenstette et al., arxiv:1101.0309; Grefenstette and Sadrzadeh, arXiv:1106.4058v1 [cs.CL]). . In this paper, we extend this study by examining transitive verbs, represented as matrices in a DisCoCat. We discuss three ways of constructing such matrices, and evaluate each method in a disambiguation task developed by Grefenstette and Sadrzadeh (arXiv:1106.4058v1 [cs.CL]).

Connected through hyperedge HE-1 (induced by source paper "collaborative training of tensors for compositional distributional semantics"):
  with members:
    - "skip thought vectors"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of sentences and documents"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed representations of words and phrases and their compositionality"
    - "multi step regression learning for compositional distributional semantics"
    - "adadelta an adaptive learning rate method"
    - "distributed tree kernels"

Connected through hyperedge HE-2 (induced by source paper "the frobenius anatomy of word meanings ii possessive relative pronouns"):
  with members:
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-3 (induced by source paper "evaluating neural word representations in tensor based compositional settings"):
  with members:
    - "a study of entanglement in a categorical framework of natural language"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-4 (induced by source paper "a study of entanglement in a categorical framework of natural language"):
  with members:
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "resolving lexical ambiguity in tensor regression models of meaning"
    - "reasoning about meaning in natural language with compact closed categories and frobenius algebras"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-5 (induced by source paper "semantic composition and decomposition from recognition to generation"):
  with members:
    - "distributional semantics beyond words supervised learning of analogy and paraphrase"
    - "domain and function a dual space model of semantic relations and compositions"
    - "from frequency to meaning vector space models of semantics"
    - "a survey of paraphrasing and textual entailment methods"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "using sentence plausibility to learn the semantics of transitive verbs"):
  with members:
    - "a study of entanglement in a categorical framework of natural language"
    - "multi step regression learning for compositional distributional semantics"
    - "adadelta an adaptive learning rate method"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-7 (induced by source paper "reasoning about meaning in natural language with compact closed categories and frobenius algebras"):
  with members:
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "category theoretic quantitative compositional distributional models of natural language semantics"):
  with members:
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "concrete sentence spaces for compositional distributional models of meaning"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

Question: Please tell me which class the center node 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)