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: "not not bad is not bad a distributional account of negation"
  Abstract: With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing. In this paper, we address shortcomings in the ability of current models to capture logical operations such as negation. As a solution we propose a tripartite formulation for a continuous vector space representation of semantics and subsequently use this representation to develop a formal compositional notion of negation within such models.

Connected through hyperedge HE-1 (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"
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-2 (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"
    - "adadelta an adaptive learning rate method"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-3 (induced by source paper "learning distributed word representations for natural logic reasoning"):
  with members:
    - "recursive neural networks can learn logical semantics"
    - "sequence to sequence learning with neural networks"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
    - "natural language processing almost from scratch"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-4 (induced by source paper "semantics modelling and the problem of representation of meaning a brief survey of recent literature"):
  with members:
    - "probabilistic similarity logic"
    - "learning dependency based compositional semantics"
    - "concrete sentence spaces for compositional distributional models 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)