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: "deep keyphrase generation"
  Abstract: Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "two huge title and keyword generation corpora of research articles"):
  with members:
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "sentiment analysis of czech texts an algorithmic survey"
    - "a review of keyphrase extraction"
    - "efficiency metrics for data driven models a text summarization case study"
    - "keyphrase generation a multi aspect survey"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "data driven summarization of scientific articles"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "keyphrase extraction with span based feature representations"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "keyphrase generation a multi aspect survey"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "a review of keyphrase extraction"
    - "keyphrase generation a text summarization struggle"
    - "efficiency metrics for data driven models a text summarization case study"
    - "deep reinforcement learning for sequence to sequence models"
    - "keyphrase generation with correlation constraints"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"

Connected through hyperedge HE-4 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-5 (induced by source paper "neural keyphrase generation via reinforcement learning with adaptive rewards"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "microblog hashtag generation via encoding conversation contexts"
    - "keyphrase generation with correlation constraints"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "semi supervised learning for neural keyphrase generation"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "multi head attention with disagreement regularization"
    - "generating diverse numbers of diverse keyphrases"

Connected through hyperedge HE-6 (induced by source paper "efficiency metrics for data driven models a text summarization case study"):
  with members:
    - "measuring the data efficiency of deep learning methods"
    - "deep reinforcement learning for sequence to sequence models"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "global encoding for abstractive summarization"
    - "know what you don t know unanswerable questions for squad"
    - "training tips for the transformer model"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "divgraphpointer a graph pointer network for extracting diverse keyphrases"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "convolutional sequence to sequence learning"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "semi supervised learning for neural keyphrase generation"
    - "generating diverse numbers of diverse keyphrases"
    - "title guided encoding for keyphrase generation"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "scientific information extraction with semi supervised neural tagging"

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)