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 deep neural network for chinese zero pronoun resolution"
  Abstract: This paper investigates the problem of Chinese zero pronoun resolution. Most existing approaches are based on machine learning algorithms, using hand-crafted features, which is labor-intensive. More- over, semantic information that is essential in the resolution of noun phrases has not been addressed enough by previous approaches on zero pronoun resolution. This is because that zero pronouns have no descriptive information, which makes it almost impossible to calculate semantic similarity between the zero pronoun and its candidate antecedents. To deal with these problems, we aim at exploring learn- ing algorithms that are capable of generating semantic representations for zero pronouns, capturing the intricate related- ness between zero pronouns and candidate antecedents, and meanwhile less dependent on extensive feature engineering. To achieve this goal, in this paper, we propose a zero pronoun-specific neural network for Chinese zero pronoun resolution task. Experimental results show that our approach significantly outperforms the state-of-the- art method.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for chinese zero pronoun resolution"):
  with members:
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "generating and exploiting large scale pseudo training data for zero pronoun resolution"
    - "deep reinforcement learning for dialogue generation"
    - "deep reinforcement learning for mention ranking coreference models"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neural recovery machine for chinese dropped pronoun"):
  with members:
    - "a novel approach to dropped pronoun translation"
    - "distributed representations of words and phrases and their compositionality"

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)