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: "compressive hyperspectral imaging with side information"
  Abstract: A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.

Connected through hyperedge HE-1 (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"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"
    - "coded aperture compressive temporal imaging"

Connected through hyperedge HE-2 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "an efficient algorithm for compression based compressed sensing"
    - "deep fully connected networks for video compressive sensing"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "block wise lensless compressive camera"
    - "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"
    - "a deep learning approach to structured signal recovery"
    - "lensless compressive imaging"

Connected through hyperedge HE-4 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"

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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "block wise lensless compressive camera"):
  with members:
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "lensless compressive imaging"
    - "flatcam thin bare sensor cameras using coded aperture and computation"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "compressive hyperspectral imaging via approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "approximate message passing in coded aperture snapshot spectral imaging"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

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)