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: "query and output generating words by querying distributed word representations for paraphrase generation"
  Abstract: Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"

Connected through hyperedge HE-2 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-3 (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"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "livebot generating live video comments based on visual and textual contexts"):
  with members:
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "attention is all you need"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "analyzing the behavior of visual question answering models"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "bag of words as target for neural machine translation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "neural summarization by extracting sentences and words"

Connected through hyperedge HE-6 (induced by source paper "improving neural question generation using answer separation"):
  with members:
    - "entity commonsense representation for neural abstractive summarization"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "question answering and question generation as dual tasks"
    - "bilateral multi perspective matching for natural language sentences"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-7 (induced by source paper "autoencoder as assistant supervisor improving text representation for chinese social media text summarization"):
  with members:
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "get to the point summarization with pointer generator networks"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-8 (induced by source paper "unsupervised machine commenting with neural variational topic model"):
  with members:
    - "automatic article commenting the task and dataset"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "sengen sentence generating neural variational topic model"
    - "clustering is efficient for approximate maximum inner product search"
    - "neural variational inference for text processing"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "quantization based fast inner product search"
    - "adam a method for stochastic optimization"

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)