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: "kea practical automatic keyphrase extraction"
  Abstract: Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents. We use a large test corpus to evaluate Kea's effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available.

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"
    - "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"

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"
    - "deep keyphrase generation"
    - "a structured self attentive sentence embedding"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "domain adaptation with adversarial training and graph embeddings"
    - "recurrent neural network for text classification with multi task learning"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "complex network based supervised keyword extractor"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "xgboost a scalable tree boosting system"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "statistical keyword detection in literary corpora"
    - "smote synthetic minority over sampling technique"
    - "learning to extract keyphrases from text"

Connected through hyperedge HE-5 (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-6 (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-7 (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-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"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"

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.DL(Digital Libraries)