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 blind video super resolution"):
  Abstract: Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images. In this paper, we propose a deep convolutional neural network (CNN) model to solve video SR by a blur kernel modeling approach. The proposed deep CNN model consists of motion blur estimation, motion estimation, and latent image restoration modules. The motion blur estimation module is used to provide reliable blur kernels. With the estimated blur kernel, we develop an image deconvolution method based on the image formation model of video SR to generate intermediate latent images so that some sharp image contents can be restored well. However, the generated intermediate latent images may contain artifacts. To generate high-quality images, we use the motion estimation module to explore the information from adjacent frames, where the motion estimation can constrain the deep CNN model for better image restoration. We show that the proposed algorithm is able to generate clearer images with finer structural details. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.

Includes members:
  Member "blind super resolution with iterative kernel correction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta transfer learning for zero shot super resolution")
  Member "blind super resolution kernel estimation using an internal gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explorable super resolution")
  Member "generative adversarial networks and perceptual losses for video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cdgan cyclic discriminative generative adversarial networks for image to image transformation")
  Member "recurrent back projection network for video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zooming slow mo fast and accurate one stage space time video super resolution")
  Member "edvr video restoration with enhanced deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blurry video frame interpolation")
  Member "video enhancement with task oriented flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "softmax splatting for video frame interpolation")
  Member "neural blind deconvolution using deep priors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep url a model aware approach to blind deconvolution based on deep unfolded richardson lucy network")
  Member "frame recurrent video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "restore from restored video restoration with pseudo clean video")

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)