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: "james stein type center pixel weights for non local means image denoising"
  Abstract: Non-Local Means (NLM) and its variants have proven to be effective and robust in many image denoising tasks. In this letter, we study approaches to selecting center pixel weights (CPW) in NLM. Our key contributions are 1) we give a novel formulation of the CPW problem from a statistical shrinkage perspective; 2) we construct the James-Stein shrinkage estimator in the CPW context; and 3) we propose a new local James-Stein type CPW (LJSCPW) that is locally tuned for each image pixel. Our experimental results showed that compared to existing CPW solutions, the LJSCPW is more robust and effective under various noise levels. In particular, the NLM with the LJSCPW attains higher means with smaller variances in terms of the peak signal and noise ratio (PSNR) and structural similarity (SSIM), implying it improves the NLM denoising performance and makes the denoising less sensitive to parameter changes.

Connected through hyperedge HE-1 (induced by source paper "linearized admm and fast nonlocal denoising for efficient plug and play restoration"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "poisson inverse problems by the plug and play scheme"
    - "compressive imaging via approximate message passing with image denoising"

Connected through hyperedge HE-2 (induced by source paper "pruned non local means"):
  with members:
    - "boosting of image denoising algorithms"
    - "non local patch regression robust image denoising in patch space"
    - "non local euclidean medians"

Connected through hyperedge HE-3 (induced by source paper "stabilizing dual energy x ray computed tomography reconstructions using patch based regularization"):
  with members:
    - "a parametric level set approach to simultaneous object identification and background reconstruction for dual energy computed tomography"

Connected through hyperedge HE-4 (induced by source paper "fast blockwise sure shrinkage for image denoising"):
  with members:
    - "probabilistic non local means"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "on the convergence of the irls algorithm in non local patch regression"
    - "non local euclidean medians"

Connected through hyperedge HE-5 (induced by source paper "probabilistic non local means"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "on the convergence of the irls algorithm in non local patch regression"
    - "non local euclidean medians"

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)