human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"

Connected through hyperedge HE-3 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "gated fusion network for joint image deblurring and super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "effects of blur and deblurring to visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "real time mdnet"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "scale recurrent network for deep image deblurring"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-5 (induced by source paper "a deep optimization approach for image deconvolution"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning an optimizer for image deconvolution"
    - "scale recurrent network for deep image deblurring"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "learning a discriminative prior for blind image deblurring"
    - "blind image deconvolution using deep generative priors"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal filter adaptive network for video deblurring"):
  with members:
    - "davanet stereo deblurring with view aggregation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "video frame interpolation via adaptive convolution"
    - "simultaneous stereo video deblurring and scene flow estimation"

Connected through hyperedge HE-7 (induced by source paper "deep video deblurring the devil is in the details"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-8 (induced by source paper "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"

Question: Please tell me which class the center node 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)