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 shot unsupervised cross domain translation"
  Abstract: Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B. We argue that this task could be a key AI capability that underlines the ability of cognitive agents to act in the world and present empirical evidence that the existing unsupervised domain translation methods fail on this task. Our method follows a two step process. First, a variational autoencoder for domain B is trained. Then, given the new sample x, we create a variational autoencoder for domain A by adapting the layers that are close to the image in order to directly fit x, and only indirectly adapt the other layers. Our experiments indicate that the new method does as well, when trained on one sample x, as the existing domain transfer methods, when these enjoy a multitude of training samples from domain A. Our code is made publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "video generation from single semantic label map"
    - "pixel adaptive convolutional neural networks"
    - "dance dance generation motion transfer for internet videos"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-2 (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"
    - "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"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-3 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-4 (induced by source paper "one shot image to image translation via part global learning with a multi adversarial framework"):
  with members:
    - "generative adversarial network with multi branch discriminator for cross species image to image translation"
    - "semantic image retrieval by uniting deep neural networks and cognitive architectures"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "few shot adaptation for multimedia semantic indexing"

Connected through hyperedge HE-5 (induced by source paper "learning to transfer unsupervised meta domain translation"):
  with members:
    - "optimal unsupervised domain translation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "multimodal unsupervised image to image translation"
    - "unsupervised learning via meta learning"
    - "meta learning update rules for unsupervised representation learning"
    - "conditional image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-6 (induced by source paper "zstgan an adversarial approach for unsupervised zero shot image to image translation"):
  with members:
    - "exploring explicit domain supervision for latent space disentanglement in unpaired image to image translation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "zero shot learning via class conditioned deep generative models"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "bidirectional one shot unsupervised domain mapping"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "nam non adversarial unsupervised domain mapping"
    - "drit diverse image to image translation via disentangled representations"
    - "image to image translation for domain adaptation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "probabilistic plant modeling via multi view image to image translation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"
    - "stereoscopic neural style transfer"
    - "improving shape deformation in unsupervised image to image translation"
    - "conditional image to image translation"

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)