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: "algorithm induced prior for image restoration"
  Abstract: This paper studies a type of image priors that are constructed implicitly through the alternating direction method of multiplier (ADMM) algorithm, called the algorithm-induced prior. Different from classical image priors which are defined before running the reconstruction algorithm, algorithm-induced priors are defined by the denoising procedure used to replace one of the two modules in the ADMM algorithm. Since such prior is not explicitly defined, analyzing the performance has been difficult in the past. #R##N#Focusing on the class of symmetric smoothing filters, this paper presents an explicit expression of the prior induced by the ADMM algorithm. The new prior is reminiscent to the conventional graph Laplacian but with stronger reconstruction performance. It can also be shown that the overall reconstruction has an efficient closed-form implementation if the associated symmetric smoothing filter is low rank. The results are validated with experiments on image inpainting.

Connected through hyperedge HE-1 (induced by source paper "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"):
  with members:
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "adaptive image denoising by targeted databases"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-2 (induced by source paper "acceleration of red via vector extrapolation"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-3 (induced by source paper "plug and play admm for image restoration fixed point convergence and applications"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "compressive imaging via approximate message passing with image denoising"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"
    - "depth reconstruction from sparse samples representation algorithm and sampling"

Connected through hyperedge HE-4 (induced by source paper "the little engine that could regularization by denoising red"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "image restoration with locally selected class adapted models"
    - "postprocessing of compressed images via sequential denoising"
    - "boosting of image denoising algorithms"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-5 (induced by source paper "a gaussian mixture mrf for model based iterative reconstruction with applications to low dose x ray ct"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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)