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: "generating diverse numbers of diverse keyphrases"
  Abstract: Existing keyphrase generation studies suffer from the problems of generating duplicate phrases and deficient evaluation based on a fixed number of predicted phrases. We propose a recurrent generative model that generates multiple keyphrases sequentially from a text, with specific modules that promote generation diversity. We further propose two new metrics that consider a variable number of phrases. With both existing and proposed evaluation setups, our model demonstrates superior performance to baselines on three types of keyphrase generation datasets, including two newly introduced in this work: StackExchange and TextWorld ACG. In contrast to previous keyphrase generation approaches, our model generates sets of diverse keyphrases of a variable number.

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"
    - "title guided encoding for keyphrase generation"

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"
    - "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-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"
    - "title guided encoding for keyphrase generation"
    - "attention is all you need"
    - "deep keyphrase generation"

Connected through hyperedge HE-5 (induced by source paper "ledeepchef deep reinforcement learning agent for families of text based games"):
  with members:
    - "comprehensible context driven text game playing"
    - "learn how to cook a new recipe in a new house using map familiarization curriculum learning and common sense to learn families of text based adventure games"
    - "textworld a learning environment for text based games"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "text based adventures of the golovin ai agent"

Connected through hyperedge HE-6 (induced by source paper "building dynamic knowledge graphs from text based games"):
  with members:
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "one shot relational learning for knowledge graphs"
    - "attention is all you need"
    - "pointing the unknown words"

Connected through hyperedge HE-7 (induced by source paper "keyphrase generation for scientific articles using gans"):
  with members:
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "deep keyphrase generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "effective approaches to attention based neural machine translation"

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)