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: "generative adversarial network for abstractive text summarization"
  Abstract: In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.

Connected through hyperedge HE-1 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"

Connected through hyperedge HE-2 (induced by source paper "improving abstractive text summarization with history aggregation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "text summarization with pretrained encoders"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "in conclusion not repetition comprehensive abstractive summarization with diversified attention based on determinantal point processes"):
  with members:
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "representation degeneration problem in training natural language generation models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "bottom up abstractive summarization"
    - "wikihow a large scale text summarization dataset"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-4 (induced by source paper "controllable length control neural encoder decoder via reinforcement learning"):
  with members:
    - "structure infused copy mechanisms for abstractive summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "get to the point summarization with pointer generator networks"
    - "faithful to the original fact aware neural abstractive summarization"
    - "controllable abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-5 (induced by source paper "a novel repetition normalized adversarial reward for headline generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural document summarization by jointly learning to score and select sentences"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "language as a latent variable discrete generative models for sentence compression"

Connected through hyperedge HE-6 (induced by source paper "topic augmented generator for abstractive summarization"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "wikihow a large scale text summarization dataset"
    - "efficient summarization with read again and copy mechanism"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "eliciting knowledge from experts automatic transcript parsing for cognitive task analysis"):
  with members:
    - "deep contextualized word representations"
    - "learning cognitive models using neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "natural language inference with hierarchical bilstm max pooling architecture"
    - "empower sequence labeling with task aware neural language model"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "sequence to sequence rnns for text summarization"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "deep adversarial social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the effectiveness of least squares generative adversarial networks"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "gradient estimation using stochastic computation graphs"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

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)