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: "functional distributional semantics"
  Abstract: Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.

Connected through hyperedge HE-1 (induced by source paper "semantic composition via probabilistic model theory"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "variational inference for logical inference"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "representing meaning with a combination of logical and distributional models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-2 (induced by source paper "variational inference for logical inference"):
  with members:
    - "semantic composition via probabilistic model theory"
    - "representing meaning with a combination of logical and distributional models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "expectation propagation for approximate bayesian inference"
    - "from frequency to meaning vector space models of semantics"
    - "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)