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: "keyphrase generation with correlation constraints"
  Abstract: In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting in duplication and coverage issues. To solve these problems, we propose a new sequence-to-sequence architecture for keyphrase generation named CorrRNN, which captures correlation among multiple keyphrases in two ways. First, we employ a coverage vector to indicate whether the word in the source document has been summarized by previous phrases to improve the coverage for keyphrases. Second, preceding phrases are taken into account to eliminate duplicate phrases and improve result coherence. Experiment results show that our model significantly outperforms the state-of-the-art method on benchmark datasets in terms of both accuracy and diversity.

Connected through hyperedge HE-1 (induced by source paper "large scale evaluation of keyphrase extraction models"):
  with members:
    - "kptimes a large scale dataset for keyphrase generation on news documents"
    - "content selection in deep learning models of summarization"
    - "allennlp a deep semantic natural language processing platform"
    - "semi supervised learning for neural keyphrase generation"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "keyphrase extraction with span based feature representations"):
  with members:
    - "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"
    - "deep keyphrase generation"
    - "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"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "semi supervised learning for neural keyphrase generation"

Connected through hyperedge HE-4 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "adversarial training for multi context joint entity and relation extraction"

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"
    - "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"
    - "title guided encoding for keyphrase generation"

Connected through hyperedge HE-6 (induced by source paper "divgraphpointer a graph pointer network for extracting diverse keyphrases"):
  with members:
    - "deep keyphrase generation"
    - "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-7 (induced by source paper "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"):
  with members:
    - "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"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"
    - "scientific information extraction with semi supervised neural tagging"

Connected through hyperedge HE-8 (induced by source paper "does order matter an empirical study on generating multiple keyphrases as a sequence"):
  with members:
    - "keyphrase generation a text summarization struggle"
    - "divgraphpointer a graph pointer network for extracting diverse keyphrases"
    - "bottom up abstractive summarization"
    - "one size does not fit all generating and evaluating variable number of keyphrases"
    - "semi supervised learning for neural keyphrase generation"
    - "the importance of generation order in language modeling"
    - "neural models for key phrase detection and question generation"
    - "deep keyphrase generation"

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)