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: "towards neural speaker modeling in multi party conversation the task dataset and models"
  Abstract: Neural network-based dialog systems are attracting increasing attention in both academia and industry. Recently, researchers have begun to realize the importance of speaker modeling in neural dialog systems, but there lacks established tasks and datasets. In this paper, we propose speaker classification as a surrogate task for general speaker modeling, and collect massive data to facilitate research in this direction. We further investigate temporal-based and content-based models of speakers, and propose several hybrids of them. Experiments show that speaker classification is feasible, and that hybrid models outperform each single component.

Connected through hyperedge HE-1 (induced by source paper "gsn a graph structured network for multi party dialogues"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "dynamic routing between capsules"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "a persona based neural conversation model"
    - "lstm based conversation models"

Connected through hyperedge HE-2 (induced by source paper "addressee and response selection in multi party conversations with speaker interaction rnns"):
  with members:
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "coherent dialogue with attention based language models"
    - "learning end to end goal oriented dialog"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)