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: "adaptive image denoising by targeted databases"
  Abstract: We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains relevant patches. We formulate the denoising problem as an optimal filter design problem and make two contributions. First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem. The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance. Improvement methods are proposed to enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior. The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation. We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images, and face images. Experimental results show the superiority of the new algorithm over existing methods.

Connected through hyperedge HE-1 (induced by source paper "self supervised fast adaptation for denoising via meta learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "residual dense network for image restoration"
    - "on first order meta learning algorithms"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-2 (induced by source paper "restore from restored single image denoising with pseudo clean image"):
  with members:
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "residual dense network for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-3 (induced by source paper "real image denoising with feature attention"):
  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"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "renoir a dataset for real low light image noise reduction"
    - "unprocessing images for learned raw denoising"

Connected through hyperedge HE-4 (induced by source paper "color image and multispectral image denoising using block diagonal representation"):
  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"
    - "renoir a dataset for real low light image noise reduction"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (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-6 (induced by source paper "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"):
  with members:
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "algorithm induced prior for image restoration"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "external prior guided internal prior learning for real world noisy image denoising"):
  with members:
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "removing mixture of gaussian and impulse noise by patch based weighted means"
    - "poisson noise reduction with non local pca"

Connected through hyperedge HE-8 (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"

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)