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: "decoding history based adaptive control of attention for neural machine translation"
  Abstract: Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the English-Vietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at this https URL

Connected through hyperedge HE-1 (induced by source paper "deconvolution based global decoding for neural machine translation"):
  with members:
    - "global encoding for abstractive summarization"
    - "label embedding network learning label representation for soft training of deep networks"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "deconvolutional latent variable model for text sequence matching"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"
    - "deconvolutional paragraph representation learning"

Connected through hyperedge HE-2 (induced by source paper "bag of words as target for neural machine translation"):
  with members:
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "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-3 (induced by source paper "word embedding attention network generating words by querying distributed word representations for paraphrase generation"):
  with members:
    - "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"
    - "joint copying and restricted generation for paraphrase"
    - "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-4 (induced by source paper "future prediction based model for neural machine translation"):
  with members:
    - "bag of words as target for neural machine translation"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"
    - "attention is all you need"
    - "multi channel encoder for neural machine translation"
    - "coverage embedding models for neural machine translation"
    - "interactive attention for neural machine translation"
    - "neural machine translation in linear time"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "softtarget regularization an effective technique to reduce over fitting in neural networks"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-6 (induced by source paper "global encoding for abstractive summarization"):
  with members:
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"):
  with members:
    - "global encoding for abstractive summarization"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "table to text generation by structure aware seq2seq learning"
    - "memory enhanced decoder for neural machine translation"
    - "coverage embedding models for neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "demystifying neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"
    - "perceptual losses for real time style transfer and super resolution"
    - "sequence to sequence learning with neural networks"

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)