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: "structural group sparse representation for image compressive sensing recovery"
  Abstract: Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet, contourlet and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain, thus greatly confining the CS solution space. In addition, an efficient iterative shrinkage/thresholding algorithm based technique is developed to solve the above optimization problem. Experimental results demonstrate that the novel CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence.

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 "joint group and residual sparse coding for image compressive sensing"):
  with members:
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "group based sparse representation for image compressive sensing reconstruction with non convex regularization"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-3 (induced by source paper "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to block based compressed sensing of images"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "atgv net accurate depth super resolution"

Connected through hyperedge HE-4 (induced by source paper "ista net interpretable optimization inspired deep network for image compressive sensing"):
  with members:
    - "learning deep cnn denoiser prior for image restoration"
    - "deep convolutional neural network for inverse problems in imaging"
    - "a deep learning approach to block based compressed sensing of images"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "atgv net accurate depth super resolution"
    - "tensorflow a system for large scale machine learning"
    - "a deep learning approach to structured signal recovery"
    - "on learning optimized reaction diffusion processes for effective image restoration"

Connected through hyperedge HE-5 (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"
    - "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"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-6 (induced by source paper "analyzing the group sparsity based on the rank minimization methods"):
  with members:
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "group based sparse representation for image restoration"
    - "image processing using smooth ordering of its patches"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "group based sparse representation for image restoration"):
  with members:
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "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 "image restoration using joint statistical modeling in a space transform domain"):
  with members:
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "improved total variation based image compressive sensing recovery by nonlocal regularization"
    - "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)