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: "semi supervised learning for neural keyphrase generation"
  Abstract: We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a selflearning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.

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"
    - "keyphrase generation with correlation constraints"
    - "content selection in deep learning models of summarization"
    - "allennlp a deep semantic natural language processing platform"
    - "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 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-3 (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"
    - "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-4 (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"
    - "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-5 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  with members:
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "denoising distant supervision for relation extraction via instance level adversarial training"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "keyphrase generation with correlation constraints"
    - "bottom up abstractive summarization"
    - "one size does not fit all generating and evaluating variable number of keyphrases"
    - "the importance of generation order in language modeling"
    - "neural models for key phrase detection and question generation"
    - "deep keyphrase generation"

Connected through hyperedge HE-8 (induced by source paper "open domain web keyphrase extraction beyond language modeling"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "deep contextualized word representations"
    - "automatic event salience identification"
    - "generating diverse numbers of diverse keyphrases"
    - "title guided encoding for keyphrase generation"
    - "attention is all you need"
    - "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)