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: "experimental support for a categorical compositional distributional model of meaning"
  Abstract: Modelling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. We implement the abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using data from the BNC and evaluate it. The implementation is based on unsupervised learning of matrices for relational words and applying them to the vectors of their arguments. The evaluation is based on the word disambiguation task developed by Mitchell and Lapata (2008) for intransitive sentences, and on a similar new experiment designed for transitive sentences. Our model matches the results of its competitors in the first experiment, and betters them in the second. The general improvement in results with increase in syntactic complexity showcases the compositional power of our model.

Connected through hyperedge HE-1 (induced by source paper "incremental monoidal grammars"):
  with members:
    - "exploring semantic incrementality with dynamic syntax and vector space semantics"
    - "thue s 1914 paper a translation"
    - "generalizing determinization from automata to coalgebras"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-2 (induced by source paper "compositional generalization in image captioning"):
  with members:
    - "the curious case of neural text degeneration"
    - "universal dependency parsing from scratch"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"
    - "assessing composition in sentence vector representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-3 (induced by source paper "cbow is not all you need combining cbow with the compositional matrix space model"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-4 (induced by source paper "the mathematics of text structure"):
  with members:
    - "compositionality for recursive neural networks"
    - "static and dynamic vector semantics for lambda calculus models of natural language"
    - "towards compositional distributional discourse analysis"
    - "classical copying versus quantum entanglement in natural language the case of vp ellipsis"
    - "internal wiring of cartesian verbs and prepositions"
    - "from quantum foundations via natural language meaning to a theory of everything"
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "coordination in categorical compositional distributional semantics"

Connected through hyperedge HE-5 (induced by source paper "interacting conceptual spaces i grammatical composition of concepts"):
  with members:
    - "a thorough formalization of conceptual spaces"
    - "custom hypergraph categories via generalized relations"
    - "words concepts and the geometry of analogy"
    - "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"
    - "reasoning about meaning in natural language with compact closed categories and frobenius algebras"

Connected through hyperedge HE-6 (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  with members:
    - "cos960 a chinese word similarity dataset of 960 word pairs"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-7 (induced by source paper "don t blame distributional semantics if it can t do entailment"):
  with members:
    - "deep contextualized word representations"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"
    - "multi step regression learning for compositional distributional 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 "density matrices with metric for derivational ambiguity"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "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)