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: "resolving lexical ambiguity in tensor regression models of meaning"
  Abstract: This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition. In contrast with previous research where this hypothesis has been successfully tested against relatively simple compositional models, in our work we use a robust model trained with linear regression. The results we get in two experiments show the superiority of the prior disambiguation method and suggest that the effectiveness of this approach is model-independent.

Connected through hyperedge HE-1 (induced by source paper "determining semantic textual similarity using natural deduction proofs"):
  with members:
    - "distributional inclusion hypothesis for tensor based composition"
    - "learning distributed representations of sentences from unlabelled data"
    - "distributed representations of sentences and documents"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-2 (induced by source paper "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"):
  with members:
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "evaluating neural word representations in tensor based compositional settings"
    - "compositional distributional semantics with compact closed categories and frobenius algebras"
    - "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-3 (induced by source paper "adaptive joint learning of compositional and non compositional phrase embeddings"):
  with members:
    - "improved semantic representations from tree structured long short term memory networks"
    - "evaluating neural word representations in tensor based compositional settings"
    - "using sentence plausibility to learn the semantics of transitive verbs"
    - "sequence to sequence learning with neural networks"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-4 (induced by source paper "syntax aware multi sense word embeddings for deep compositional models of meaning"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "deep learning face representation by joint identification verification"
    - "investigating the role of prior disambiguation in deep learning compositional models of meaning"
    - "evaluating neural word representations in tensor based compositional settings"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "compositional distributional semantics with compact closed categories and frobenius algebras"):
  with members:
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "a frobenius model of information structure in categorical compositional distributional semantics"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "open system categorical quantum semantics in natural language processing"
    - "compositional operators in distributional semantics"
    - "a study of entanglement in a categorical framework of natural language"
    - "investigating the role of prior disambiguation in deep learning compositional models of meaning"
    - "evaluating neural word representations in tensor based compositional settings"

Connected through hyperedge HE-6 (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"
    - "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"
    - "experimenting with transitive verbs in a discocat"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-7 (induced by source paper "investigating the role of prior disambiguation in deep learning compositional models of meaning"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "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)