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 restoration using joint statistical modeling in a space transform domain"
  Abstract: This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-fold. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving the image inverse problem is formulated using JSM under a regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split Bregman-based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring, and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "learning deep cnn denoiser prior for image restoration"
    - "enhanced low rank matrix approximation"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

Connected through hyperedge HE-2 (induced by source paper "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "enhanced low rank matrix approximation"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "robust pca via nonconvex rank approximation"
    - "group based sparse representation for image restoration"

Connected through hyperedge HE-3 (induced by source paper "group sparsity residual constraint for image denoising"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "non convex weighted lp minimization based group sparse representation framework for image denoising"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "adaptive image denoising by mixture adaptation"
    - "adaptive image denoising by targeted databases"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "boosting of image denoising algorithms"
    - "group based sparse representation for image restoration"

Connected through hyperedge HE-4 (induced by source paper "image denoising using group sparsity residual and external nonlocal self similarity prior"):
  with members:
    - "group sparsity residual constraint for image denoising"
    - "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"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "adaptive image denoising by mixture adaptation"
    - "image denoising via group sparsity residual constraint"
    - "group based sparse representation for image restoration"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-5 (induced by source paper "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"):
  with members:
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "group based sparse representation for image restoration"
    - "robust subspace system identification via weighted nuclear norm optimization"
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "non convex weighted lp nuclear norm based admm framework for image restoration"):
  with members:
    - "analyzing the group sparsity based on the rank minimization methods"
    - "group based sparse representation for image restoration"
    - "a fast patch dictionary method for whole image recovery"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "a comparative study for the nuclear norms minimization methods"):
  with members:
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "non convex weighted lp nuclear norm based admm framework for image restoration"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust pca via nonconvex rank approximation"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-8 (induced by source paper "image restoration using group sparse representation via weighted nuclear norm minimization"):
  with members:
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "a fast patch dictionary method for whole image recovery"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "robust principal component analysis"

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.MM(Multimedia)