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: "gsn a graph structured network for multi party dialogues"
  Abstract: Existing neural models for dialogue response generation assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors (i.e., multi-party dialogues), where the assumption does not hold as utterances from different interlocutors can occur "in parallel." This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is a graph-based encoder that can model the information flow along the graph-structured dialogues (two-party sequential dialogues are a special case). Experimental results show that GSN significantly outperforms existing sequence-based models.

Connected through hyperedge HE-1 (induced by source paper "how to write summaries with patterns learning towards abstractive summarization through prototype editing"):
  with members:
    - "abstractive text summarization by incorporating reader comments"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "learning to write with cooperative discriminators"
    - "response generation by context aware prototype editing"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "iterative document representation learning towards summarization with polishing"

Connected through hyperedge HE-2 (induced by source paper "flexner a flexible lstm cnn stack framework for named entity recognition"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a discourse level named entity recognition and relation extraction dataset for chinese literature text"
    - "neural system combination for machine translation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "improving multilingual named entity recognition with wikipedia entity type mapping"

Connected through hyperedge HE-3 (induced by source paper "who did they respond to conversation structure modeling using masked hierarchical transformer"):
  with members:
    - "domain adaptive training bert for response selection"
    - "a large scale corpus for conversation disentanglement"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "augmenting neural response generation with context aware topical attention"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "an annotation scheme of a large scale multi party dialogues dataset for discourse parsing and machine comprehension"):
  with members:
    - "a deep sequential model for discourse parsing on multi party dialogues"
    - "coqa a conversational question answering challenge"
    - "quac question answering in context"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "newsqa a machine comprehension dataset"
    - "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)