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: "simultaneous stereo video deblurring and scene flow estimation"
  Abstract: Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods. We evaluate our method extensively on two available datasets and achieve significant improvement in flow estimation and removing the blur effect over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-2 (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"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-3 (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"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "learning a discriminative prior for blind image deblurring"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "online video deblurring via dynamic temporal blending network"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"

Connected through hyperedge HE-5 (induced by source paper "neural blind deconvolution using deep priors"):
  with members:
    - "simultaneous fidelity and regularization learning for image restoration"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "scale recurrent network for deep image deblurring"
    - "learning to deblur images with exemplars"
    - "learning a discriminative prior for blind image deblurring"
    - "online video deblurring via dynamic temporal blending network"
    - "deep image prior"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "pcan 3d attention map learning using contextual information for point cloud based retrieval"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"

Connected through hyperedge HE-7 (induced by source paper "single image deblurring and camera motion estimation with depth map"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "learning a discriminative prior for blind image deblurring"
    - "bringing alive blurred moments"
    - "online video deblurring via dynamic temporal blending network"
    - "self supervised learning for stereo matching with self improving ability"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"

Connected through hyperedge HE-8 (induced by source paper "deep stacked hierarchical multi patch network for image deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "a deeper look at power normalizations"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "stereo video deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "hand held video deblurring via efficient fourier aggregation"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

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)