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: "sentence entailment in compositional distributional semantics"
  Abstract: Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.

Connected through hyperedge HE-1 (induced by source paper "siamese recurrent networks learn first order logic reasoning and exhibit zero shot compositional generalization"):
  with members:
    - "deep learning a critical appraisal"
    - "can neural networks understand logical entailment"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "learning explanatory rules from noisy data"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning continuous semantic representations of symbolic expressions"
    - "building machines that learn and think like people"

Connected through hyperedge HE-2 (induced by source paper "density matrices with metric for derivational ambiguity"):
  with members:
    - "open system categorical quantum semantics in natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "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 "translating and evolving towards a model of language change in discocat"):
  with members:
    - "interacting conceptual spaces i grammatical composition of concepts"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"
    - "graded entailment for compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "from logical to distributional models"
    - "exploiting similarities among languages for machine translation"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-4 (induced by source paper "semantic composition via probabilistic model theory"):
  with members:
    - "variational inference for logical inference"
    - "functional distributional semantics"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "representing meaning with a combination of logical and distributional models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-5 (induced by source paper "linguistic matrix theory"):
  with members:
    - "distributional inclusion hypothesis for tensor based composition"
    - "open system categorical quantum semantics in natural language processing"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-6 (induced by source paper "distributional inclusion hypothesis for tensor based composition"):
  with members:
    - "graded entailment for compositional distributional semantics"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "a study of entanglement in a categorical framework of natural language"
    - "evaluating neural word representations in tensor based compositional settings"
    - "a convolutional neural network for modelling sentences"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-7 (induced by source paper "compositional distributional cognition"):
  with members:
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "interacting conceptual spaces"
    - "graded entailment for compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "evaluating neural word representations in tensor based compositional settings"
    - "a quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure 15"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "a compositional distributional semantics two concrete constructions and some experimental evaluations"

Connected through hyperedge HE-8 (induced by source paper "graded entailment for compositional distributional semantics"):
  with members:
    - "open system categorical quantum semantics in natural language processing"
    - "distributional sentence entailment using density matrices"
    - "compositional distributional semantics with compact closed categories and frobenius algebras"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "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)