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: "joint copying and restricted generation for paraphrase"
  Abstract: Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder. The copying decoder finds the position to be copied based on a typical attention model. The generative decoder produces words limited in the source-specific vocabulary. To combine the two decoders and determine the final output, we develop a predictor to predict the mode of copying or rewriting. This predictor can be guided by the actual writing mode in the training data. We conduct extensive experiments on two different paraphrase datasets. The result shows that our model outperforms the state-of-the-art approaches in terms of both informativeness and language quality.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-2 (induced by source paper "asking questions the human way scalable question answer generation from text corpus"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "generating question answer hierarchies"
    - "improving neural question generation using world knowledge"
    - "the curious case of neural text degeneration"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "zero shot paraphrase generation with multilingual language models"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "decomposable neural paraphrase generation"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "the missing ingredient in zero shot neural machine translation"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-4 (induced by source paper "decomposable neural paraphrase generation"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "a task in a suit and a tie paraphrase generation with semantic augmentation"
    - "learning neural templates for text generation"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "towards diverse paraphrase generation using multi class wasserstein gan"):
  with members:
    - "d page diverse paraphrase generation"
    - "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"
    - "generating sentences from a continuous space"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "improving scientific article visibility by neural title simplification"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "data driven summarization of scientific articles"
    - "dynamic multi level multi task learning for sentence simplification"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "word embedding attention network generating words by querying distributed word representations for paraphrase generation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "table to text generation by structure aware seq2seq learning"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-8 (induced by source paper "redecode framework for iterative improvement in paraphrase generation"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "a deep generative framework for paraphrase generation"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"

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)