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 "integrating disparate sources of experts for robust image denoising"):
  Abstract: We study an image denoising problem: Given a set of image denoisers, each having a different denoising capability, can we design a framework that allows us to integrate the individual denoisers to produce an overall better result? If we can do so, then potentially we can integrate multiple weak denoisers to denoise complex scenes. The goal of this paper is to present a meta-procedure called the Consensus Neural Network (ConsensusNet). Given a set of initial denoisers, ConsensusNet takes the initial estimates and generates a linear combination of the results. The combined estimate is then fed to a booster neural network to reduce the amount of method noise. ConsensusNet is a modular framework that allows any image denoiser to be used in the initial stage. Experimental results show that ConsensusNet can consistently improve denoising performance for both deterministic denoisers and neural network denoisers.

Includes members:
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "non local color image denoising with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta transfer learning for zero shot super resolution")
  Member "learning deconvolution network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)