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 via group sparsity residual constraint"
  Abstract: Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.

Connected through hyperedge HE-1 (induced by source paper "from rank estimation to rank approximation rank residual constraint for image denoising"):
  with members:
    - "the perception distortion tradeoff"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "adaptive image denoising by mixture adaptation"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "deep residual learning for image recognition"

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"
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"

Connected through hyperedge HE-3 (induced by source paper "the power of complementary regularizers image recovery via transform learning and low rank modeling"):
  with members:
    - "non local recurrent network for image restoration"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "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"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "learning super resolution jointly from external and internal examples"

Connected through hyperedge HE-4 (induced by source paper "block matching convolutional neural network for image denoising"):
  with members:
    - "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"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deeply recursive convolutional network for image super resolution"

Connected through hyperedge HE-5 (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"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-6 (induced by source paper "group based sparse representation for image compressive sensing reconstruction with non convex regularization"):
  with members:
    - "analyzing the group sparsity based on the rank minimization methods"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "compressive hyperspectral imaging with side information"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "structural group sparse representation for image compressive sensing recovery"
    - "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)