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 "revisiting non local sparse models for image restoration"):
  Abstract: We propose a differentiable algorithm for image restoration inspired by the success of sparse models and self-similarity priors for natural images. Our approach builds upon the concept of joint sparsity between groups of similar image patches, and we show how this simple idea can be implemented in a differentiable architecture, allowing end-to-end training. The algorithm has the advantage of being interpretable, performing sparse decompositions of image patches, while being more parameter efficient than recent deep learning methods. We evaluate our algorithm on grayscale and color denoising, where we achieve competitive results, and on demoisaicking, where we outperform the most recent state-of-the-art deep learning model with 47 times less parameters and a much shallower architecture.

Includes members:
  Member "residual non local attention networks for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "iterative joint image demosaicking and denoising using a residual denoising network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pixel adaptive filtering units")
  Member "ffdnet toward a fast and flexible solution for cnn based image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "neural nearest neighbors networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "non local recurrent network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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)