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: "realtime time synchronized event based stereo"
  Abstract: In this work, we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera. Our method uses the velocity of the camera and a range of disparities to synchronize the positions of the events, as if they were captured at a single point in time. We represent these events using a pair of novel time synchronized event disparity volumes, which we show remove motion blur for pixels at the correct disparity in the volume, while further blurring pixels at the wrong disparity. We then apply a novel matching cost over these time synchronized event disparity volumes, which both rewards similarity between the volumes while penalizing blurriness. We show that our method outperforms more expensive, smoothing based event stereo methods, by evaluating on the Multi Vehicle Stereo Event Camera dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "end to end learning of representations for asynchronous event based data"):
  with members:
    - "event based vision a survey"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "asynchronous photometric feature tracking using events and frames"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-4 (induced by source paper "focus is all you need loss functions for event based vision"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "continuous time visual inertial odometry for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "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"
    - "unsupervised event based learning of optical flow depth and egomotion"

Connected through hyperedge HE-5 (induced by source paper "opencl based fpga accelerator for disparity map generation with stereoscopic event cameras"):
  with members:
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"

Connected through hyperedge HE-6 (induced by source paper "ev segnet semantic segmentation for event based cameras"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "cnn based segmentation of medical imaging data"
    - "pseudo labels for supervised learning on dynamic vision sensor data applied to object detection under ego motion"

Connected through hyperedge HE-7 (induced by source paper "unsupervised event based learning of optical flow depth and egomotion"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "recurrent neural network for learning densedepth and ego motion from video"
    - "event based vision meets deep learning on steering prediction for self driving cars"

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)