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: "category theoretic quantitative compositional distributional models of natural language semantics"
  Abstract: This thesis is about the problem of compositionality in distributional semantics. Distributional semantics presupposes that the meanings of words are a function of their occurrences in textual contexts. It models words as distributions over these contexts and represents them as vectors in high dimensional spaces. The problem of compositionality for such models concerns itself with how to produce representations for larger units of text by composing the representations of smaller units of text. #R##N#This thesis focuses on a particular approach to this compositionality problem, namely using the categorical framework developed by Coecke, Sadrzadeh, and Clark, which combines syntactic analysis formalisms with distributional semantic representations of meaning to produce syntactically motivated composition operations. This thesis shows how this approach can be theoretically extended and practically implemented to produce concrete compositional distributional models of natural language semantics. It furthermore demonstrates that such models can perform on par with, or better than, other competing approaches in the field of natural language processing. #R##N#There are three principal contributions to computational linguistics in this thesis. The first is to extend the DisCoCat framework on the syntactic front and semantic front, incorporating a number of syntactic analysis formalisms and providing learning procedures allowing for the generation of concrete compositional distributional models. The second contribution is to evaluate the models developed from the procedures presented here, showing that they outperform other compositional distributional models present in the literature. The third contribution is to show how using category theory to solve linguistic problems forms a sound basis for research, illustrated by examples of work on this topic, that also suggest directions for future research.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning semantically and additively compositional distributional representations"):
  with members:
    - "the mechanism of additive composition"
    - "classifying relations by ranking with convolutional neural networks"
    - "traversing knowledge graphs in vector space"
    - "a study of entanglement in a categorical framework of natural language"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-3 (induced by source paper "quantum algorithms for compositional natural language processing"):
  with members:
    - "graded entailment for compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "a quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure 15"
    - "compositional distributional semantics with compact closed categories and frobenius algebras"
    - "learning type driven tensor based meaning representations"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-4 (induced by source paper "group theoretical vector space model"):
  with members:
    - "representations of task assignments in distributed systems using young tableaux and symmetric groups"
    - "domain and function a dual space model of semantic relations and compositions"
    - "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-5 (induced by source paper "distributional sentence entailment using density matrices"):
  with members:
    - "open system categorical quantum semantics in natural language processing"
    - "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 "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"
    - "resolving lexical ambiguity in tensor regression models of meaning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "distributed representations for compositional semantics"):
  with members:
    - "learning multilingual word representations using a bag of words autoencoder"
    - "multilingual models for compositional distributed semantics"
    - "a convolutional neural network for modelling sentences"
    - "cutting recursive autoencoder trees"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-8 (induced by source paper "a convolutional neural network for modelling sentences"):
  with members:
    - "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"
    - "a context theoretic framework for compositionality in distributional 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)