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 a blurry frame alive at high frame rate with an event camera"
  Abstract: Event-based cameras can measure intensity changes (called `{\it events}') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relatively low frame-rate and often suffer from motion blur. A blurry image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we are able to model the blur-generation process by associating event data to a latent image. In this paper, we propose a simple and effective approach, the \textbf{Event-based Double Integral (EDI)} model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real images demonstrate the superiority of our EDI model and optimization method in comparison to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "events to video bringing modern computer vision to event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "a deep journey into super resolution a survey"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "the relativistic discriminator a key element missing from standard gan"

Connected through hyperedge HE-2 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-3 (induced by source paper "eventcap monocular 3d capture of high speed human motions using an event camera"):
  with members:
    - "event based vision a survey"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "temporal upsampling of depth maps using a hybrid camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "asynchronous photometric feature tracking using events and frames"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"

Connected through hyperedge HE-4 (induced by source paper "ced color event camera dataset"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "asynchronous spatial image convolutions for event cameras"
    - "continuous time visual inertial odometry for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "realtime time synchronized event based stereo"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"

Connected through hyperedge HE-5 (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"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "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-6 (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"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "bringing alive blurred moments"

Connected through hyperedge HE-7 (induced by source paper "asynchronous spatial image convolutions for event cameras"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"
    - "continuous time intensity estimation using event cameras"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "real time intensity image reconstruction for event cameras using manifold regularisation"

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)