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 "scale recurrent network for deep image deblurring"):
  Abstract: In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches. In this paper, we investigate this strategy and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task. Compared with the many recent learning-based approaches in [25], it has a simpler network structure, a smaller number of parameters and is easier to train. We evaluate our method on large-scale deblurring datasets with complex motion. Results show that our method can produce better quality results than state-of-the-arts, both quantitatively and qualitatively.

Includes members:
  Member "online video deblurring via dynamic temporal blending network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blurry video frame interpolation")
  Member "deep image matting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "detail revealing deep video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "softmax splatting for video frame interpolation")
  Member "fast image processing with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "real time single image and video super resolution using an efficient sub pixel convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "a neural approach to blind motion deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blur noise and compression robust generative adversarial networks")
  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 "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blur noise and compression robust generative adversarial networks")

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)