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: "d page diverse paraphrase generation"
  Abstract: In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.

Connected through hyperedge HE-1 (induced by source paper "generating diverse translation by manipulating multi head attention"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "mixture models for diverse machine translation tricks of the trade"
    - "investigating backtranslation in neural machine translation"
    - "multi head attention with disagreement regularization"
    - "convolutional self attention network"
    - "sequence to sequence mixture model for diverse machine translation"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-2 (induced by source paper "towards diverse paraphrase generation using multi class wasserstein gan"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "natural language inference over interaction space"
    - "improved training of wasserstein gans"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "joint copying and restricted generation for paraphrase"
    - "generating sentences from a continuous space"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "one size does not fit all generating and evaluating variable number of keyphrases"):
  with members:
    - "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"
    - "title guided encoding for keyphrase generation"
    - "neural models for key phrase detection and question 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)