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: "probabilistic non local means"
  Abstract: In this letter, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of the peak signal noise ratio (PSNR) and the structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the PNLM weights in tested NLM variants.

Connected through hyperedge HE-1 (induced by source paper "patch redundancy in images a statistical testing framework and some applications"):
  with members:
    - "reducing anomaly detection in images to detection in noise"
    - "texture synthesis using convolutional neural networks"
    - "learning frame models using cnn filters"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "on nearest neighbors in non local means denoising"):
  with members:
    - "block matching convolutional neural network for image denoising"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-3 (induced by source paper "fast blockwise sure shrinkage for image denoising"):
  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"
    - "non local euclidean medians"

Connected through hyperedge HE-4 (induced by source paper "fast separable non local means"):
  with members:
    - "global registration of multiple point clouds using semidefinite programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

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)