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: "concrete sentence spaces for compositional distributional models of meaning"
  Abstract: Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a compositional model of meaning for distributional semantics, in which each word in a sentence has a meaning vector and the distributional meaning of the sentence is a function of the tensor products of the word vectors. Abstractly speaking, this function is the morphism corresponding to the grammatical structure of the sentence in the category of finite dimensional vector spaces. In this paper, we provide a concrete method for implementing this linear meaning map, by constructing a corpus-based vector space for the type of sentence. Our construction method is based on structured vector spaces whereby meaning vectors of all sentences, regardless of their grammatical structure, live in the same vector space. Our proposed sentence space is the tensor product of two noun spaces, in which the basis vectors are pairs of words each augmented with a grammatical role. This enables us to compare meanings of sentences by simply taking the inner product of their vectors.

Connected through hyperedge HE-1 (induced by source paper "machine translation a literature review"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised neural machine translation"
    - "one model to learn them all"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "applying distributional compositional categorical models of meaning to language translation"):
  with members:
    - "custom hypergraph categories via generalized relations"
    - "electronic proceedings in theoretical computer science eptcs preface"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"

Connected through hyperedge HE-3 (induced by source paper "information flow in pregroup models of natural language"):
  with members:
    - "retracing some paths in process algebra"

Connected through hyperedge HE-4 (induced by source paper "the lifted matrix space model for semantic composition"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "natural language inference over interaction space"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "neural semantic encoders"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "long short term memory over tree structures"
    - "compositional distributional semantics with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "improving semantic composition with offset inference"):
  with members:
    - "learning semantically and additively compositional distributional representations"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "discriminative neural sentence modeling by tree based convolution"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed tree kernels"
    - "domain and function a dual space model of semantic relations and compositions"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-6 (induced by source paper "recursive neural networks can learn logical semantics"):
  with members:
    - "improved semantic representations from tree structured long short term memory networks"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
    - "can recursive neural tensor networks learn logical reasoning"
    - "not not bad is not bad a distributional account of negation"
    - "adadelta an adaptive learning rate method"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-7 (induced by source paper "semantics modelling and the problem of representation of meaning a brief survey of recent literature"):
  with members:
    - "not not bad is not bad a distributional account of negation"
    - "probabilistic similarity logic"
    - "learning dependency based compositional semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "a quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure 15"):
  with members:
    - "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)