human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "smart sparse contours to represent and edit images"):
  Abstract: We study the problem of reconstructing an image from information stored at sparse contour locations. Existing contour-based image reconstruction methods struggle to balance contour sparsity and reconstruction fidelity. Therefore, denser contours are needed to capture subtle texture information even though contours were not meant for textures. We propose a novel image representation where image content is characterized by contours with gradient information via an encoder-decoder network, while image details are modeled by a conditional generative adversarial network. We show that high-quality reconstructions with high fidelity to the source image can be obtained from extremely sparse input, e.g., comprising less than 6% of image pixels. Our model synthesizes texture, details and fine structures in regions where no input information is provided. The semantic knowledge encoded into our model and the sparsity of the input allows using contours as an intuitive interface for semantically-aware image manipulation: local edits in contour domain such as scaling, translation and erasing, translate to long-range and coherent changes in the pixel space. Experiments on a variety of datasets verify the versatility and convenience afforded by our models.

Includes members:
  Member "texturegan controlling deep image synthesis with texture patches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "neural photo editing with introspective adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel framework for selection of gans for an application")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "generating images with perceptual similarity metrics based on deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric image generation from layouts")
  Member "plug play generative networks conditional iterative generation of images in latent space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "opengan open set generative adversarial networks")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "least squares generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)