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 "a deep optimization approach for image deconvolution"):
  Abstract: In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be instable, or ineffective. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our network achieves competitive performance on GoPro dataset, and our model is extremely lightweight compared with the state-of-art works.

Includes members:
  Member "learning converged propagations with deep prior ensemble for image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels")
  Member "learning an optimizer for image deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention guided network for ghost free high dynamic range imaging")
  Member "scale recurrent network for deep image deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "learning a discriminative prior for blind image deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning")
  Member "blind image deconvolution using deep generative priors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "learning collaborative generation correction modules for blind image deblurring and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation")
  Member "overcoming catastrophic forgetting with hard attention to the task":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual 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)