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 "the power of complementary regularizers image recovery via transform learning and low rank modeling"):
  Abstract: Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods.

Includes members:
  Member "non local recurrent network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "multi channel weighted nuclear norm minimization for real color image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "when image denoising meets high level vision tasks a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "beyond deep residual learning for image restoration persistent homology guided manifold simplification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi level wavelet convolutional neural networks")
  Member "image denoising via group sparsity residual constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from rank estimation to rank approximation rank residual constraint for image denoising")
  Member "frist flipping and rotation invariant sparsifying transform learning and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning")

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)