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: "stereo video deblurring"
  Abstract: Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task. We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-of-the-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-4 (induced by source paper "adversarial spatio temporal learning for video deblurring"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (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"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"

Connected through hyperedge HE-6 (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"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning to extract a video sequence from a single motion blurred image"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "motion deblurring in the wild"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "perceptual losses for real time style transfer and super resolution"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "bringing a blurry frame alive at high frame rate with an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "bringing alive blurred moments"

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)