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: "dual supervised learning"
  Abstract: Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "closed loop matters dual regression networks for single image super resolution"):
  with members:
    - "intelligent home 3d automatic 3d house design from linguistic descriptions only"
    - "hierarchical neural architecture search for single image super resolution"
    - "discrimination aware network pruning for deep model compression"
    - "multi marginal wasserstein gan"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "nat neural architecture transformer for accurate and compact architectures"

Connected through hyperedge HE-2 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "dual supervised learning for natural language understanding and generation"
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-3 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-4 (induced by source paper "semantic parsing with dual learning"):
  with members:
    - "agentgraph towards universal dialogue management with structured deep reinforcement learning"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "question generation from sql queries improves neural semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "weakly supervised semantic parsing with abstract examples"

Connected through hyperedge HE-5 (induced by source paper "jointly learning semantic parser and natural language generator via dual information maximization"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "code2seq generating sequences from structured representations of code"
    - "triangle generative adversarial networks"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-6 (induced by source paper "tagged back translation"):
  with members:
    - "text repair model for neural machine translation"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "a call for clarity in reporting bleu scores"
    - "explaining and generalizing back translation through wake sleep"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-7 (induced by source paper "automatically learning data augmentation policies for dialogue tasks"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "toward controlled generation of text"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"

Connected through hyperedge HE-8 (induced by source paper "representation degeneration problem in training natural language generation models"):
  with members:
    - "frage frequency agnostic word representation"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "regularizing and optimizing lstm language models"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "all but the top simple and effective postprocessing for word representations"

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.LG(Machine Learning)