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: "analyzing the group sparsity based on the rank minimization methods"
  Abstract: Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse discriminant conditions cannot keep unchanged. This paper analyzes the sparsity of group based on the strategy of the rank minimization. Firstly, an adaptive dictionary for each group is designed. Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group is measured by estimating the singular values of each group. Based on that measurement, the weighted Schatten $p$-norm minimization (WSNM) has been found to be the closest solution to the real singular values of each group. Thus, WSNM can be equivalently transformed into a non-convex $\ell_p$-norm minimization problem in group-based sparse coding. To make the proposed scheme tractable and robust, the alternating direction method of multipliers (ADMM) is used to solve the $\ell_p$-norm minimization problem. Experimental results on two applications: image inpainting and image compressive sensing (CS) recovery have shown that the proposed scheme outperforms many state-of-the-art methods.

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"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-2 (induced by source paper "rank minimization for snapshot compressive imaging"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"
    - "coded aperture compressive temporal imaging"

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"
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "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 "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"
    - "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"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-5 (induced by source paper "beyond a gaussian denoiser residual learning of deep cnn for image denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "cascades of regression tree fields for image restoration"
    - "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"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "compression artifacts reduction by a deep convolutional network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (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"
    - "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"
    - "image restoration using joint statistical modeling in a space transform domain"

Connected through hyperedge HE-7 (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"
    - "adaptive image denoising by mixture adaptation"
    - "image denoising via group sparsity residual constraint"
    - "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-8 (induced by source paper "group based sparse representation for image compressive sensing reconstruction with non convex regularization"):
  with members:
    - "image denoising via group sparsity residual constraint"
    - "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)