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 learning for machine translation"
  Abstract: While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation \emph{dual-NMT}. Experiments show that dual-NMT works very well on English$\leftrightarrow$French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.

Connected through hyperedge HE-1 (induced by source paper "unsupervised correlation analysis"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised learning of semantic mappings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "one sided unsupervised domain mapping"
    - "word translation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "turbo learning for captionbot and drawingbot"):
  with members:
    - "reinforced video captioning with entailment rewards"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "conditional image generation with pixelcnn decoders"
    - "learning what and where to draw"

Connected through hyperedge HE-3 (induced by source paper "generalization bounds for unsupervised cross domain mapping with wgans"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "estimating the success of unsupervised image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "learning how to self learn enhancing self training using neural reinforcement learning"):
  with members:
    - "split and rephrase"
    - "learning how to active learn a deep reinforcement learning approach"
    - "sentence simplification with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "learning to translate in real time with neural machine translation"
    - "predicting the effectiveness of self training application to sentiment classification"
    - "enriching word vectors with subword information"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "non adversarial unsupervised word translation"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "word translation without parallel data"
    - "dualgan unsupervised dual learning for image to image translation"
    - "perceptual losses for real time style transfer and super resolution"
    - "enriching word vectors with subword information"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-6 (induced by source paper "semi supervised qa with generative domain adaptive nets"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "pointing the unknown words"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "review networks for caption generation"
    - "bidirectional attention flow for machine comprehension"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "maximally distant cross domain generators for estimating per sample error"):
  with members:
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "one sided unsupervised domain mapping"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-8 (induced by source paper "one sided unsupervised domain mapping"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cross domain image generation"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"

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)