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: "a new approach to keyphrase extraction using neural networks"
  Abstract: Keyphrases provide a simple way of describing a document, giving the reader some clues about its contents. Keyphrases can be useful in a various applications such as retrieval engines, browsing interfaces, thesaurus construction, text mining etc.. There are also other tasks for which keyphrases are useful, as we discuss in this paper. This paper describes a neural network based approach to keyphrase extraction from scientific articles. Our results show that the proposed method performs better than some state-of-the art keyphrase extraction approaches.

Connected through hyperedge HE-1 (induced by source paper "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"):
  with members:
    - "recurrent neural network encoder with attention for community question answering"
    - "the influence of context on dialogue act recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "key phrase extraction of lightly filtered broadcast news"):
  with members:
    - "keyphrase cloud generation of broadcast news"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-3 (induced by source paper "a hybrid approach to extract keyphrases from medical documents"):
  with members:
    - "learning algorithms for keyphrase extraction"
    - "kea practical automatic keyphrase extraction"

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.IR(Information Retrieval)