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: "building a knowledge graph from natural language definitions for interpretable text entailment recognition"
  Abstract: Natural language definitions of terms can serve as a rich source of knowledge, but structuring them into a comprehensible semantic model is essential to enable them to be used in semantic interpretation tasks. We propose a method and provide a set of tools for automatically building a graph world knowledge base from natural language definitions. Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation. WordNetGraph, a knowledge graph built out of noun and verb WordNet definitions according to this methodology, was successfully used in an interpretable text entailment recognition approach which uses paths in this graph to provide clear justifications for entailment decisions.

Connected through hyperedge HE-1 (induced by source paper "multi channel reverse dictionary model"):
  with members:
    - "enhancing recurrent neural networks with sememes"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "using multi sense vector embeddings for reverse dictionaries"
    - "incorporating glosses into neural word sense disambiguation"
    - "learning to compute word embeddings on the fly"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-2 (induced by source paper "identifying and explaining discriminative attributes"):
  with members:
    - "on the semantic interpretability of artificial intelligence models"
    - "bomji at semeval 2018 task 10 combining vector pattern and graph based information to identify discriminative attributes"
    - "luminoso at semeval 2018 task 10 distinguishing attributes using text corpora and relational knowledge"
    - "categorization of semantic roles for dictionary definitions"
    - "from frequency to meaning vector space models of semantics"

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)