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 "learning collaborative generation correction modules for blind image deblurring and beyond"):
  Abstract: Blind image deblurring plays a very important role in many vision and multimedia applications. Most existing works tend to introduce complex priors to estimate the sharp image structures for blur kernel estimation. However, it has been verified that directly optimizing these models is challenging and easy to fall into degenerate solutions. Although several experience-based heuristic inference strategies, including trained networks and designed iterations, have been developed, it is still hard to obtain theoretically guaranteed accurate solutions. In this work, a collaborative learning framework is established to address the above issues. Specifically, we first design two modules, named Generator and Corrector, to extract the intrinsic image structures from the data-driven and knowledge-based perspectives, respectively. By introducing a collaborative methodology to cascade these modules, we can strictly prove the convergence of our image propagations to a deblurring-related optimal solution. As a nontrivial byproduct, we also apply the proposed method to address other related tasks, such as image interpolation and edge-preserved smoothing. Plenty of experiments demonstrate that our method can outperform the state-of-the-art approaches on both synthetic and real datasets.

Includes members:
  Member "toward designing convergent deep operator splitting methods for task specific nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convergence of admm with task adaption and beyond")
  Member "convergence analysis of proximal gradient with momentum for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")
  Member "proximal alternating direction network a globally converged deep unrolling framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image stylization from predefined to personalized")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "learning fully convolutional networks for iterative non blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep blind video super resolution")
  Member "plug and play admm for image restoration fixed point convergence and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blind image restoration without prior knowledge")
  Member "deep multi scale convolutional neural network for dynamic scene deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "cascades of regression tree fields for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning deep gradient descent optimization for image deconvolution")

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)