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 "motion deblurring in the wild"):
  Abstract: The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions.

Includes members:
  Member "cnn for license plate motion deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deblurring face images using uncertainty guided multi stream semantic networks")
  Member "parametric object motion from blur":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep video deblurring the devil is in the details")
  Member "end to end learning for image burst deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lsd 2 joint denoising and deblurring of short and long exposure images with convolutional neural networks")
  Member "a neural approach to blind motion deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "dynamic scene deblurring using a locally adaptive linear blur model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan based image deblurring using dark channel prior")
  Member "flownet learning optical flow with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "generalized video deblurring for dynamic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blurry video frame interpolation")

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)