human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep plug and play prior for low rank tensor completion"):
  Abstract: Tensor image data sets such as color images and multispectral images are highly correlated and they contain a lot of image details. The main aim of this paper is to propose and develop a regularized tensor completion model for tensor image data completion. In the objective function, we adopt the newly emerged tensor nuclear norm (TNN) to characterize the global structure of such tensor image data sets. Also, we formulate an implicit regularizer to plug in the convolutional neural network (CNN) denoiser, which is convinced to express the image prior learned from a large amount of natural images. The resulting model can be solved efficiently via an alternating directional method of multipliers algorithm. Experimental results (on color images, videos, and multispectral images) are presented to show that both image global structure and details can be recovered very well, and to illustrate that the performance of the proposed method is better than that of testing methods in terms of PSNR and SSIM.

Includes members:
  Member "plug and play methods provably converge with properly trained denoisers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling")
  Member "deep plug and play super resolution for arbitrary blur kernels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks")
  Member "low rank tensor completion for multiway visual data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low rank tensor grid for image completion")
  Member "regularization by denoising clarifications and new interpretations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "non local meets global an integrated paradigm for hyperspectral denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")
  Member "tensor n tubal rank and its convex relaxation for low rank tensor recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")
  Member "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")
  Member "image restoration by iterative denoising and backward projections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")

Question: Please tell me which class the source paper 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)