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: "unsupervised visual attribute transfer with reconfigurable generative adversarial networks"
  Abstract: Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "targeted nonlinear adversarial perturbations in images and videos"

Connected through hyperedge HE-2 (induced by source paper "metalgan multi domain label less image synthesis using cgans and meta learning"):
  with members:
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "metalgan a cluster based adaptive training for few shot adversarial colorization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to optimize neural nets"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attgan facial attribute editing by only changing what you want"

Connected through hyperedge HE-3 (induced by source paper "changing the image memorability from basic photo editing to gans"):
  with members:
    - "video to video synthesis"
    - "amnet memorability estimation with attention"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "how to make an image more memorable a deep style transfer approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "show and recall learning what makes videos memorable"

Connected through hyperedge HE-4 (induced by source paper "conditional gans for multi illuminant color constancy revolution or yet another approach"):
  with members:
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "artificial color constancy via googlenet with angular loss function"
    - "direction aware spatial context features for shadow detection and removal"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "attgan facial attribute editing by only changing what you want"):
  with members:
    - "dna gan learning disentangled representations from multi attribute images"
    - "combogan unrestrained scalability for image domain translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "fader networks manipulating images by sliding attributes"
    - "attribute guided face generation using conditional cyclegan"

Connected through hyperedge HE-6 (induced by source paper "learning compositional visual concepts with mutual consistency"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "learning cross domain disentangled deep representation with supervision from a single domain"
    - "detach and adapt learning cross domain disentangled deep representation"
    - "attribute guided face generation using conditional cyclegan"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-7 (induced by source paper "stargan unified generative adversarial networks for multi domain image to image translation"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-8 (induced by source paper "multi view data generation without view supervision"):
  with members:
    - "unsupervised learning of disentangled representations from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "multi view image generation from a single view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "acoustic feature learning via deep variational canonical correlation analysis"
    - "fader networks manipulating images by sliding attributes"
    - "disentangling factors of variation in deep representations using 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.CV(Computer Vision and Pattern Recognition)