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: "improved total variation based image compressive sensing recovery by nonlocal regularization"
  Abstract: Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.

Connected through hyperedge HE-1 (induced by source paper "block compressive sensing of image and video with nonlocal lagrangian multiplier and patch based sparse representation"):
  with members:
    - "total variation reconstruction for compressive sensing using nonlocal lagrangian multiplier"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "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"
    - "from denoising to compressed sensing"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-2 (induced by source paper "reconnet non iterative reconstruction of images from compressively sensed random measurements"):
  with members:
    - "a deep learning approach to structured signal recovery"
    - "compressive imaging via approximate message passing with image denoising"
    - "dense optical flow prediction from a static image"
    - "designing deep networks for surface normal estimation"
    - "caffe convolutional architecture for fast feature embedding"
    - "from denoising to compressed sensing"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "total variation reconstruction for compressive sensing using nonlocal lagrangian multiplier"):
  with members:
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-4 (induced by source paper "from denoising to compressed sensing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "does ell_p minimization outperform ell_1 minimization"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bilinear generalized approximate message passing part i derivation"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "parameterless optimal approximate message passing"

Connected through hyperedge HE-5 (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"
    - "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)