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: "learning to extract a video sequence from a single motion blurred image"
  Abstract: We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulated over time during the exposure of the sensor. Unfortunately, reversing this process is nontrivial. Firstly, averaging destroys the temporal ordering of the frames. Secondly, the recovery of a single frame is a blind deconvolution task, which is highly ill-posed. We present a deep learning scheme that gradually reconstructs a temporal ordering by sequentially extracting pairs of frames. Our main contribution is to introduce loss functions invariant to the temporal order. This lets a neural network choose during training what frame to output among the possible combinations. We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. Our proposed method can successfully retrieve sharp image sequences from a single motion blurred image and can generalize well on synthetic and real datasets captured with different cameras.

Connected through hyperedge HE-1 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"

Connected through hyperedge HE-2 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "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"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"

Connected through hyperedge HE-3 (induced by source paper "visual deprojection probabilistic recovery of collapsed dimensions"):
  with members:
    - "stochastic adversarial video prediction"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "bringing alive blurred moments"
    - "online video deblurring via dynamic temporal blending network"
    - "stochastic variational video prediction"
    - "motion deblurring in the wild"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-4 (induced by source paper "event driven video frame synthesis"):
  with members:
    - "gated fusion network for joint image deblurring and super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "continuous time intensity estimation using event cameras"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "compressive holographic video"

Connected through hyperedge HE-5 (induced by source paper "high frame rate video reconstruction based on an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "computing spatial image convolutions for event cameras"
    - "scale recurrent network for deep image deblurring"
    - "phase only image based kernel estimation for single image blind deblurring"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "bringing alive blurred moments"

Connected through hyperedge HE-6 (induced by source paper "single image deblurring and camera motion estimation with depth map"):
  with members:
    - "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"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"

Connected through hyperedge HE-7 (induced by source paper "photosequencing of motion blur using short and long exposures"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "residual dense network for image super resolution"
    - "bringing alive blurred moments"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "image to image translation with conditional adversarial networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "intra frame object tracking by deblatting"):
  with members:
    - "high speed tracking with multi kernel correlation filters"
    - "tracking for half an hour"
    - "learning blind motion deblurring"
    - "the world of fast moving objects"
    - "long term visual object tracking benchmark"
    - "a novel performance evaluation methodology for single target trackers"
    - "performance measures and a data set for multi target multi camera tracking"
    - "learning spatially regularized correlation filters for visual tracking"

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)