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 euclidean medians"
  Abstract: In this letter, we note that the denoising performance of Non-Local Means (NLM) can be improved at large noise levels by replacing the mean by the Euclidean median. We call this new denoising algorithm the Non-Local Euclidean Medians (NLEM). At the heart of NLEM is the observation that the median is more robust to outliers than the mean. In particular, we provide a simple geometric insight that explains why NLEM performs better than NLM in the vicinity of edges, particularly at large noise levels. NLEM can be efficiently implemented using iteratively reweighted least squares, and its computational complexity is comparable to that of NLM. We provide some preliminary results to study the proposed algorithm and to compare it with NLM.

Connected through hyperedge HE-1 (induced by source paper "on detection of faint edges in noisy images"):
  with members:
    - "richer convolutional features for edge detection"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "fast detection of curved edges at low snr"
    - "holistically nested edge detection"

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 patch regression robust image denoising in patch space"

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:
    - "non local patch regression robust image denoising in patch space"
    - "on the convergence of the irls algorithm in non local patch regression"

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"
    - "james stein type center pixel weights for non local means image denoising"
    - "on the convergence of the irls algorithm in non local patch regression"

Connected through hyperedge HE-5 (induced by source paper "an approach for reducing outliers of non local means image denoising filter"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"

Connected through hyperedge HE-6 (induced by source paper "probabilistic non local means"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "james stein type center pixel weights for non local means image denoising"
    - "on the convergence of the irls algorithm in non local patch regression"

Connected through hyperedge HE-7 (induced by source paper "non local patch regression robust image denoising in patch space"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the convergence of the irls algorithm in non local patch regression"
    - "sparse recovery by non convex optimization instance optimality"

Connected through hyperedge HE-8 (induced by source paper "on the convergence of the irls algorithm in non local patch regression"):
  with members:
    - "non local patch regression robust image denoising in patch space"

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)