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: "image denoising using group sparsity residual and external nonlocal self similarity prior"
  Abstract: Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. Due to the fact that the groups contain a large amount of NSS information of natural images, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture model (GMM) learning and the group sparse coefficients of noisy image is used to approximate the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

Connected through hyperedge HE-1 (induced by source paper "kernel free image deblurring with a pair of blurred noisy images"):
  with members:
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "image restoration with locally selected class adapted models"
    - "recent progress in image deblurring"

Connected through hyperedge HE-2 (induced by source paper "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "from rank estimation to rank approximation rank residual constraint for image denoising"
    - "group sparsity residual with non local samples for image denoising"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "fast resampling of three dimensional point clouds via graphs"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-3 (induced by source paper "dual reweighted lp norm minimization for salt and pepper noise removal"):
  with members:
    - "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "image denoising using new adaptive based median filters"
    - "generalized singular value thresholding"
    - "stable principal component pursuit"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "group sparsity residual with non local samples for image denoising"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "adaptive image denoising by mixture adaptation"
    - "group based sparse representation for image restoration"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

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)