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: "nam non adversarial unsupervised domain mapping"
  Abstract: Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source domain is indistinguishable from the target domain, which suffers from known stability issues. In addition, most methods rely heavily on `cycle' relationships between the domains, which enforce a one-to-one mapping. In this work, we introduce an alternative method: Non-Adversarial Mapping (NAM), which separates the task of target domain generative modeling from the cross-domain mapping task. NAM relies on a pre-trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function. It has several key advantages: higher quality and resolution image translations, simpler and more stable training and reusable target models. Extensive experiments are presented validating the advantages of our method.

Connected through hyperedge HE-1 (induced by source paper "mimicgan robust projection onto image manifolds with corruption mimicking"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "universal decision based black box perturbations breaking security through obscurity defenses"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-2 (induced by source paper "degas differentiable efficient generator search"):
  with members:
    - "agan towards automated design of generative adversarial networks"
    - "autogan neural architecture search for generative adversarial networks"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "logan unpaired shape transform in latent overcomplete space"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "p2p net bidirectional point displacement net for shape transform"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "point convolutional neural networks by extension operators"
    - "multimodal unsupervised image to image translation"
    - "pcpnet learning local shape properties from raw point clouds"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-4 (induced by source paper "style generator inversion for image enhancement and animation"):
  with members:
    - "structured gans"
    - "video to video synthesis"
    - "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"
    - "non adversarial image synthesis with generative latent nearest neighbors"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-5 (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"
    - "one shot unsupervised cross domain translation"
    - "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-6 (induced by source paper "demystifying inter class disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "generating diverse high fidelity images with vq vae 2"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "disentangling factors of variation with cycle consistent variational auto encoders"

Connected through hyperedge HE-7 (induced by source paper "crossnet latent cross consistency for unpaired image translation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "specular to diffuse translation for multi view reconstruction"
    - "unsupervised attention guided 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"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "lrs dag low resource supervised domain adaptation with generalization across domains"):
  with members:
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "cycada cycle consistent adversarial domain adaptation"
    - "few shot adversarial domain adaptation"
    - "the numerics of gans"
    - "unified deep supervised domain adaptation and generalization"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "associative domain adaptation"

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)