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 "pruned non local means"):
  Abstract: In non-local means (NLM), each pixel is denoised by performing a weighted averaging of its neighbouring pixels, where the weights are computed using image patches. The authors demonstrate that the denoising performance of NLM can be improved by pruning the neighbouring pixels, namely, by rejecting neighbouring pixels whose weights are below a certain threshold.. While pruning can potentially reduce pixel averaging in uniform-intensity regions, they demonstrate that there is generally an overall improvement in the denoising performance. In particular, the improvement comes from pixels situated close to edges and corners. The success of the proposed method strongly depends on the choice of the global threshold., which in turn depends on the noise level and the image characteristics. They show how Stein's unbiased estimator of the mean-squared error can be used to optimally tune., at a marginal computational overhead. They present some representative denoising results to demonstrate the superior performance of the proposed method over NLM and its variants.

Includes members:
  Member "boosting of image denoising algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep k svd denoising")
  Member "james stein type center pixel weights for non local means image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linearized admm and fast nonlocal denoising for efficient plug and play restoration")
  Member "non local patch regression robust image denoising in patch space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defogging kinect simultaneous estimation of object region and depth in foggy scenes")
  Member "non local euclidean medians":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)