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: "bringing alive blurred moments"
  Abstract: We present a solution for the novel goal of extracting a video from a single blurred image to sequentially reconstruct the views of a scene as beheld by the camera during the time of exposure. We approach this task by first learning a motion representation for videos in an unsupervised manner through training of a novel video autoencoder network that performs a surrogate task of video reconstruction. Instead of enforc- ing temporal constraints on features learned at the image level, we em- ploy a recurrent design with convolutional filters to directly learn tempo- rally correlated representations. Once trained, this network is employed for the guided training of a motion encoder for blurred images. This net- work extracts embedded motion information from a single blurred image so as to generate a sharp video in conjunction with the trained recur- rent video decoder. Experiments on real scenes and standard data-sets demonstrate our framework's ability to generate a plausible sequence of sharp frames.

Connected through hyperedge HE-1 (induced by source paper "visual deprojection probabilistic recovery of collapsed dimensions"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "stochastic adversarial video prediction"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "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-2 (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"
    - "learning to extract a video sequence from a single motion blurred image"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "photosequencing of motion blur using short and long exposures"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "residual dense network for image super resolution"
    - "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-5 (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"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"

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)