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: "monte carlo non local means random sampling for large scale image filtering"
  Abstract: We propose a randomized version of the nonlocal means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo nonlocal means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. We make two contributions. First, we analyze the performance of the MCNLM algorithm and show that, for large images or large external image databases, the random outcomes of MCNLM are tightly concentrated around the deterministic full NLM result. In particular, our error probability bounds show that, at any given sampling ratio, the probability for MCNLM to have a large deviation from the original NLM solution decays exponentially as the size of the image or database grows. Second, we derive explicit formulas for optimal sampling patterns that minimize the error probability bound by exploiting partial knowledge of the pairwise similarity weights. Numerical experiments show that MCNLM is competitive with other state-of-the-art fast NLM algorithms for single-image denoising. When applied to denoising images using an external database containing ten billion patches, MCNLM returns a randomized solution that is within 0.2 dB of the full NLM solution while reducing the runtime by three orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "external patch based image restoration using importance sampling"):
  with members:
    - "poisson image denoising using best linear prediction a post processing framework"
    - "class specific image denoising using importance sampling"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep class aware denoising"
    - "deep convolutional denoising of low light images"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"

Connected through hyperedge HE-2 (induced by source paper "chaining identity mapping modules for image denoising"):
  with members:
    - "group sparsity residual constraint for image denoising"
    - "robust 3d action recognition through sampling local appearances and global distributions"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"

Connected through hyperedge HE-3 (induced by source paper "optimal combination of image denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "adaptive image denoising by mixture adaptation"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-4 (induced by source paper "understanding symmetric smoothing filters a gaussian mixture model perspective"):
  with members:
    - "adaptive image denoising by mixture adaptation"
    - "a gaussian mixture mrf for model based iterative reconstruction with applications to low dose x ray ct"
    - "adaptive image denoising by targeted databases"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-5 (induced by source paper "algorithm induced prior for image restoration"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "postprocessing of compressed images via sequential denoising"
    - "approximate message passing with universal denoising"
    - "demystifying symmetric smoothing filters"
    - "from denoising to compressed sensing"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "adaptive image denoising by mixture adaptation"):
  with members:
    - "demystifying symmetric smoothing filters"
    - "adaptive image denoising by targeted databases"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "adaptive image denoising by targeted databases"):
  with members:
    - "image processing using smooth ordering of its patches"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "depth reconstruction from sparse samples representation algorithm and sampling"):
  with members:
    - "a probabilistic and ripless theory of compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)