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: "smit stochastic multi label image to image translation"
  Abstract: Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the case of multiple labels. This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality associated with the translation. Most of the existing state-of-the-art has focused only on two reasons, i.e. producing disentangled representations from unpaired datasets in a one-to-one domain translation or producing multiple unimodal attributes from unpaired datasets. In this work, we propose a joint framework of diversity and multi-mapping image-to-image translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Extensive experiments over different datasets demonstrate the effectiveness of our proposed approach with comparisons to the state-of-the-art in both multi-label and multimodal problems. Additionally, our method is able to generalize under different scenarios: continuous style interpolation, continuous label interpolation, and multi-label mapping.

Connected through hyperedge HE-1 (induced by source paper "stargan v2 diverse image synthesis for multiple domains"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "tag2pix line art colorization using text tag with secat and changing loss"
    - "multi mapping image to image translation via learning disentanglement"
    - "large scale adversarial representation learning"
    - "few shot unsupervised image to image translation"
    - "sdit scalable and diverse cross domain image translation"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-2 (induced by source paper "multi mapping image to image translation via learning disentanglement"):
  with members:
    - "stargan v2 diverse image synthesis for multiple domains"
    - "sdit scalable and diverse cross domain image translation"
    - "cgans with projection discriminator"
    - "multi mapping image to image translation with central biasing normalization"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "cr gan learning complete representations for multi view generation"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  with members:
    - "video generation from single semantic label map"
    - "guided image to image translation with bi directional feature transformation"
    - "pose guided human video generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "video prediction with appearance and motion conditions"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "flow grounded spatial temporal video prediction from still images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "pi rec progressive image reconstruction network with edge and color domain"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "attribute guided unpaired image to image translation with semi supervised learning"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "multimodal unsupervised image to image translation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "label noise robust multi domain image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "deep learning is robust to massive label noise"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "label noise robust generative adversarial networks"
    - "on self modulation for generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "class distinct and class mutual image generation with gans"):
  with members:
    - "cgans with projection discriminator"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "generative adversarial image synthesis with decision tree latent controller"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-8 (induced by source paper "high resolution deep convolutional generative adversarial networks"):
  with members:
    - "video to video synthesis"
    - "faceshop deep sketch based face image editing"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "deep appearance models for face rendering"
    - "which training methods for gans do actually converge"
    - "generative image inpainting with contextual attention"

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)