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: "non local patch regression robust image denoising in patch space"
  Abstract: It was recently demonstrated in [13] that the denoising performance of Non-Local Means (NLM) can be improved at large noise levels by replacing the mean by the robust Euclidean median. Numerical experiments on synthetic and natural images showed that the latter consistently performed better than NLM beyond a certain noise level, and significantly so for images with sharp edges. The Euclidean mean and median can be put into a common regression (on the patch space) framework, in which the l2 norm of the residuals is considered in the former, while the l1 norm is considered in the latter. The natural question then is what happens if we consider lp (0 <; p <; 1) regression? We investigate this possibility in this paper.

Connected through hyperedge HE-1 (induced by source paper "defogging kinect simultaneous estimation of object region and depth in foggy scenes"):
  with members:
    - "tackling 3d tof artifacts through learning and the flat dataset"
    - "densely connected pyramid dehazing network"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-2 (induced by source paper "pruned non local means"):
  with members:
    - "boosting of image denoising algorithms"
    - "james stein type center pixel weights for non local means image denoising"
    - "non local euclidean medians"

Connected through hyperedge HE-3 (induced by source paper "a new admm algorithm for the euclidean median and its application to robust patch regression"):
  with members:
    - "on the convergence of the irls algorithm in non local patch regression"
    - "non local euclidean medians"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of the irls algorithm in non local patch regression"):
  with members:
    - "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)