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 "fast blockwise sure shrinkage for image denoising"):
  Abstract: In this paper, we investigate the shrinkage problem of image denoising for various methods under the additive white Gaussian noise (AWGN) model. Our main contribution is to derive the closed-form of the optimal shrinkage that minimizes the Stein's unbiased risk estimator (SURE) and thus allows direct blockwise shrinkage without additional optimizations. Simulation results show that the proposed method boosts the denoising performance for a variety of image denoising techniques including the moving average filter, the median filter, the wiener filter, the bilateral filter, the probabilistic non-local means, and the block matching 3D filter in terms of higher pixel signal noise ratio (PSNR) and structural similarity index (SSIM). We also case study the proposed shrinkage solution with respect to the classic NLM denoising, whose shrinkage solutions and equivalent forms have been widely researched, and further confirm its superiority. The proposed shrinkage solution can be used to improve arbitrary image denoising methods under the AWGN model, and it serves as a good remedy to save badly denoised images due to inappropriate parameters. HighlightsWe derive the closed-form optimal blockwise SURE shrinkage for image denoising.We propose an adaptive and fast blockwise shrinkage algorithm with a constant-time complexity.We demonstrate this shrinkage solution is effective to a wide collection of image denoising methods.

Includes members:
  Member "probabilistic non local means":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patch redundancy in images a statistical testing framework and some applications")
  Member "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast high dimensional bilateral and nonlocal means filtering")
  Member "james stein type center pixel weights for non local means image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linearized admm and fast nonlocal denoising for efficient plug and play restoration")
  Member "on the convergence of the irls algorithm in non local patch regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a new admm algorithm for the euclidean median and its application to robust patch regression")
  Member "non local euclidean medians":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on detection of faint edges in noisy images")

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)