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: "investigating the role of prior disambiguation in deep learning compositional models of meaning"
  Abstract: This paper aims to explore the effect of prior disambiguation on neural network- based compositional models, with the hope that better semantic representations for text compounds can be produced. We disambiguate the input word vectors before they are fed into a compositional deep net. A series of evaluations shows the positive effect of prior disambiguation for such deep models.

Connected through hyperedge HE-1 (induced by source paper "compositional vector space models for knowledge base completion"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning longer memory in recurrent neural networks"
    - "grammar as a foreign language"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "sequence to sequence learning with neural networks"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-2 (induced by source paper "syntax aware multi sense word embeddings for deep compositional models of meaning"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "resolving lexical ambiguity in tensor regression models of meaning"
    - "deep learning face representation by joint identification verification"
    - "evaluating neural word representations in tensor based compositional settings"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (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"
    - "evaluating neural word representations in tensor based compositional settings"

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)