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 "joint demosaicing and denoising with perceptual optimization on a generative adversarial network"):
  Abstract: Image demosaicing - one of the most important early stages in digital camera pipelines - addressed the problem of reconstructing a full-resolution image from so-called color-filter-arrays. Despite tremendous progress made in the pase decade, a fundamental issue that remains to be addressed is how to assure the visual quality of reconstructed images especially in the presence of noise corruption. Inspired by recent advances in generative adversarial networks (GAN), we present a novel deep learning approach toward joint demosaicing and denoising (JDD) with perceptual optimization in order to ensure the visual quality of reconstructed images. The key contributions of this work include: 1) we have developed a GAN-based approach toward image demosacing in which a discriminator network with both perceptual and adversarial loss functions are used for quality assurance; 2) we propose to optimize the perceptual quality of reconstructed images by the proposed GAN in an end-to-end manner. Such end-to-end optimization of GAN is particularly effective for jointly exploiting the gain brought by each modular component (e.g., residue learning in the generative network and perceptual loss in the discriminator network). Our extensive experimental results have shown convincingly improved performance over existing state-of-the-art methods in terms of both subjective and objective quality metrics with a comparable computational cost.

Includes members:
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")

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)