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: "a fast patch dictionary method for whole image recovery"
  Abstract: Various algorithms have been proposed for dictionary learning. Among those for image processing, many use image patches to form dictionaries. This paper focuses on whole-image recovery from corrupted linear measurements. We address the open issue of representing an image by overlapping patches: the overlapping leads to an excessive number of dictionary coefficients to determine. With very few exceptions, this issue has limited the applications of image-patch methods to the local kind of tasks such as denoising, inpainting, cartoon-texture decomposition, super-resolution, and image deblurring, for which one can process a few patches at a time. Our focus is global imaging tasks such as compressive sensing and medical image recovery, where the whole image is encoded together, making it either impossible or very ineffective to update a few patches at a time. #R##N#Our strategy is to divide the sparse recovery into multiple subproblems, each of which handles a subset of non-overlapping patches, and then the results of the subproblems are averaged to yield the final recovery. This simple strategy is surprisingly effective in terms of both quality and speed. In addition, we accelerate computation of the learned dictionary by applying a recent block proximal-gradient method, which not only has a lower per-iteration complexity but also takes fewer iterations to converge, compared to the current state-of-the-art. We also establish that our algorithm globally converges to a stationary point. Numerical results on synthetic data demonstrate that our algorithm can recover a more faithful dictionary than two state-of-the-art methods. #R##N#Combining our whole-image recovery and dictionary-learning methods, we numerically simulate image inpainting, compressive sensing recovery, and deblurring. Our recovery is more faithful than those of a total variation method and a method based on overlapping patches.

Connected through hyperedge HE-1 (induced by source paper "differentiable linearized admm"):
  with members:
    - "k meansnet when k means meets differentiable programming"
    - "lifted proximal operator machines"
    - "optimization algorithm inspired deep neural network structure design"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "deep image prior"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "unrolled optimization with deep priors"

Connected through hyperedge HE-2 (induced by source paper "analysis of fast structured dictionary learning"):
  with members:
    - "learning multi layer transform models"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-3 (induced by source paper "analysis of fast alternating minimization for structured dictionary learning"):
  with members:
    - "analysis of fast structured dictionary learning"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-4 (induced by source paper "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "convolutional sparse coding for high dynamic range imaging"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "sparse modeling for image and vision processing"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-5 (induced by source paper "online convolutional dictionary learning"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "convolutional dictionary learning acceleration and convergence"
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning for multimodal imaging"
    - "scalable online convolutional sparse coding"
    - "a survey of sparse representation algorithms and applications"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-6 (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"
    - "from denoising to compressed sensing"
    - "improved total variation based image compressive sensing recovery by nonlocal regularization"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-7 (induced by source paper "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "non convex weighted lp nuclear norm based admm framework for image restoration"):
  with members:
    - "analyzing the group sparsity based on the rank minimization methods"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "exact matrix completion via convex optimization"

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)