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: "a deep generative framework for paraphrase generation"
  Abstract: Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed method is based on a combination of deep generative models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases, given an input sentence. Traditional VAEs when combined with recurrent neural networks can generate free text but they are not suitable for paraphrase generation for a given sentence. We address this problem by conditioning the both, encoder and decoder sides of VAE, on the original sentence, so that it can generate the given sentence's paraphrases. Unlike most existing models, our model is simple, modular and can generate multiple paraphrases, for a given sentence. Quantitative evaluation of the proposed method on a benchmark paraphrase dataset demonstrates its efficacy, and its performance improvement over the state-of-the-art methods by a significant margin, whereas qualitative human evaluation indicate that the generated paraphrases are well-formed, grammatically correct, and are relevant to the input sentence. Furthermore, we evaluate our method on a newly released question paraphrase dataset, and establish a new baseline for future research.

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 "exploring neural models for parsing natural language into first order logic"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "attention is all you need"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning a neural semantic parser from user feedback"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-3 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-4 (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"
    - "d page diverse paraphrase generation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "style transfer for texts retrain report errors compare with rewrites"):
  with members:
    - "unsupervised paraphrasing without translation"
    - "style transfer as unsupervised machine translation"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"

Connected through hyperedge HE-6 (induced by source paper "paws paraphrase adversaries from word scrambling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "assessing composition in sentence vector representations"
    - "gender bias in coreference resolution"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "open subtitles paraphrase corpus for six languages"

Connected through hyperedge HE-7 (induced by source paper "fixing gaussian mixture vaes for interpretable text generation"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "spherical latent spaces for stable variational autoencoders"
    - "toward controlled generation of text"
    - "key value retrieval networks for task oriented dialogue"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "latent intention dialogue models"

Connected through hyperedge HE-8 (induced by source paper "generating sentences from disentangled syntactic and semantic spaces"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bag of words as target for neural machine translation"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "syntax directed variational autoencoder for structured data"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text 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)