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: "title guided encoding for keyphrase generation"
  Abstract: Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "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-4 (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"
    - "semi supervised learning for neural keyphrase generation"
    - "generating diverse numbers of diverse keyphrases"
    - "attention is all you need"
    - "deep keyphrase generation"

Connected through hyperedge HE-5 (induced by source paper "topic aware neural keyphrase generation for social media language"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "microblog hashtag generation via encoding conversation contexts"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "keyphrase generation with correlation constraints"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "microblog hashtag generation via encoding conversation contexts"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "semi supervised learning for neural keyphrase generation"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "opennmt open source toolkit for neural machine translation"
    - "deep keyphrase generation"
    - "bidirectional attention flow for machine comprehension"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end memory networks"

Connected through hyperedge HE-7 (induced by source paper "one size does not fit all generating and evaluating variable number of keyphrases"):
  with members:
    - "d page diverse paraphrase generation"
    - "keyphrase generation with correlation constraints"
    - "textworld a learning environment for text based games"
    - "semi supervised learning for neural keyphrase generation"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "representation learning with contrastive predictive coding"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-8 (induced by source paper "difficulty controllable generation of reading comprehension questions"):
  with members:
    - "generating distractors for reading comprehension questions from real examinations"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "quase sequence editing under quantifiable guidance"
    - "learning to ask neural question generation for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "neural question generation from text a preliminary study"

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)