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: "a context theoretic framework for compositionality in distributional semantics"
  Abstract: Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. #R##N#In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts in which it occurs in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings. #R##N#We relate the framework to existing methods of composing vector-based representations of meaning, and show that our approach generalises many of these, including vector addition, component-wise multiplication, and the tensor product.

Connected through hyperedge HE-1 (induced by source paper "the mechanism of additive composition"):
  with members:
    - "composing distributed representations of relational patterns"
    - "learning semantically and additively compositional distributional representations"
    - "a latent variable model approach to pmi based word embeddings"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "word emdeddings through hellinger pca"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-2 (induced by source paper "aligning packed dependency trees a theory of composition for distributional semantics"):
  with members:
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "multi step regression learning for compositional distributional semantics"
    - "domain and function a dual space model of semantic relations and compositions"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"
    - "measures of distributional similarity"

Connected through hyperedge HE-3 (induced by source paper "bilingual distributed word representations from document aligned comparable data"):
  with members:
    - "skip thought vectors"
    - "learning to understand phrases by embedding the dictionary"
    - "modeling order in neural word embeddings at scale"
    - "distributed representations of sentences and documents"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "improving zero shot learning by mitigating the hubness problem"
    - "multilingual models for compositional distributed semantics"
    - "evaluating neural word representations in tensor based compositional settings"

Connected through hyperedge HE-4 (induced by source paper "resolving references to objects in photographs using the words as classifiers model"):
  with members:
    - "generation and comprehension of unambiguous object descriptions"
    - "language models for image captioning the quirks and what works"
    - "natural language object retrieval"
    - "going deeper with convolutions"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"
    - "show and tell a neural image caption generator"
    - "scalable object detection using deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a convolutional neural network for modelling sentences"):
  with members:
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-6 (induced by source paper "distributional semantics beyond words supervised learning of analogy and paraphrase"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "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"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "the latent relation mapping engine algorithm and experiments"
    - "expressing implicit semantic relations without supervision"

Connected through hyperedge HE-7 (induced by source paper "learning type driven tensor based meaning representations"):
  with members:
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "challenges for distributional compositional semantics"):
  with members:
    - "concrete sentence spaces for compositional distributional models of meaning"
    - "measures of distributional similarity"

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)