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: "one sided unsupervised domain mapping"
  Abstract: In unsupervised domain mapping, the learner is given two unmatched datasets $A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample in $A$ to the analog sample in $B$. Recent approaches have shown that when learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$, convincing mappings are obtained. In this work, we present a method of learning $G_{AB}$ without learning $G_{BA}$. This is done by learning a mapping that maintains the distance between a pair of samples. Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping. We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint. Our entire code is made publicly available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "adversarial code learning for image generation"):
  with members:
    - "image transformer"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "autosem automatic task selection and mixing in multi task learning"
    - "a survey of domain adaptation for neural machine translation"
    - "deep visual domain adaptation a survey"
    - "multi source domain adaptation with mixture of experts"
    - "unsupervised domain adaptation based on source guided discrepancy"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-3 (induced by source paper "a little fog for a large turn"):
  with members:
    - "natural adversarial examples"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"
    - "adversarial patch"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "exploring the limits of weakly supervised pretraining"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-4 (induced by source paper "unpaired image to image translation using adversarial consistency loss"):
  with members:
    - "breaking the cycle colleagues are all you need"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised attention guided image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "combogan unrestrained scalability for image domain translation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "one to one mapping for unpaired image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "towards instance level image to image translation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "analyzing inverse problems with invertible neural networks"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content transfer"):
  with members:
    - "a two step disentanglement method"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "dida disentangled synthesis for domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-7 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-8 (induced by source paper "few shot unsupervised image to image translation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "on first order meta learning algorithms"
    - "multimodal unsupervised image to image translation"
    - "one shot unsupervised cross domain translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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.CV(Computer Vision and Pattern Recognition)