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 compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
  Abstract: From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain. Most of the conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet and gradient domain) for the entirety of a signal, which are irrespective of the non-stationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor CS recovery performance. In this paper, we propose a new framework for image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization. The intrinsic sparsity of natural images is enforced substantially by sparsely representing overlapped image patches using the adaptively learned sparsifying basis in the form of L0 norm, greatly reducing blocking artifacts and confining the CS solution space. To make our proposed scheme tractable and robust, a split Bregman iteration based technique is developed to solve the non-convex L0 minimization problem efficiently. Experimental results on a wide range of natural images for CS recovery have shown that our proposed algorithm achieves significant performance improvements over many current state-of-the-art schemes and exhibits good convergence property. Incorporate patch-based redundant sparse representation into compressive sensing recovery of natural images.Characterize the redundant sparse representation using adaptively learned sparsifying basis via L0 norm.Propose a new framework for image compressive sensing recovery using adaptive learned sparsifying basis via L0 minimization.Develop a split Bregman iteration based method to solve the above L0 minimization efficiently.

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 "soft video multicasting using adaptive compressed sensing"):
  with members:
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"

Connected through hyperedge HE-3 (induced by source paper "sparse optimization problem with s difference regularization"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "image reconstruction from few views by l0 norm optimization"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "decreasing weighted sorted ell_1 regularization"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (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"
    - "structural group sparse representation for image compressive sensing recovery"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-5 (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-6 (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-7 (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-8 (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"
    - "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)