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: "on the convergence of the irls algorithm in non local patch regression"
  Abstract: Recently, it was demonstrated in , that the robustness of the classical Non-Local Means (NLM) algorithm can be improved by incorporating lp (0 <; p ≤ 2) regression into the NLM framework. This general optimization framework, called Non-Local Patch Regression (NLPR), contains NLM as a special case. Denoising results on synthetic and natural images show that NLPR consistently performs better than NLM beyond a moderate noise level, and significantly so when p is close to zero. An iteratively reweighted least-squares (IRLS) algorithm was proposed for solving the regression problem in NLPR, where the NLM output was used to initialize the iterations. Based on exhaustive numerical experiments, we observe that the IRLS algorithm is globally convergent (for arbitrary initialization) in the convex regime 1 ≤ p ≤ 2, and locally convergent (e.g., fails rarely using NLM initialization) in the non-convex regime 0 <; p <; 1. In this letter, we study the cost associated with the IRLS updates, and this, along with the framework of bounded optimization, is used to analyze the convergence of the algorithm.

Connected through hyperedge HE-1 (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"
    - "non local euclidean medians"

Connected through hyperedge HE-2 (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"
    - "non local euclidean medians"

Connected through hyperedge HE-3 (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"
    - "non local euclidean medians"

Connected through hyperedge HE-4 (induced by source paper "non local patch regression robust image denoising in patch space"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "non local euclidean medians"
    - "sparse recovery by non convex optimization instance optimality"

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)