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: "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.

Connected through hyperedge HE-1 (induced by source paper "deep learning for image denoising a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "group normalization"
    - "a variational u net for conditional appearance and shape generation"
    - "residual dense network for image super resolution"
    - "correction by projection denoising images with generative adversarial networks"
    - "dilated deep residual network for image denoising"

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)