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: "continuous time intensity estimation using event cameras"
  Abstract: Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods.

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 "high speed and high dynamic range video with an event camera"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "ced color event camera dataset"
    - "event based vision a survey"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "yolov3 an incremental improvement"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "events to video bringing modern computer vision to event cameras"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"
    - "yolov3 an incremental improvement"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "asynchronous photometric feature tracking using events and frames"

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"
    - "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-6 (induced by source paper "learning to super resolve intensity images from events"):
  with members:
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "recurrent back projection network for video super resolution"
    - "frame recurrent video super resolution"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-7 (induced by source paper "asynchronous tracking by detection on adaptive time surfaces for event based object tracking"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "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"
    - "real time clustering and multi target tracking using event based sensors"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-8 (induced by source paper "event based motion segmentation by motion compensation"):
  with members:
    - "event based vision a survey"
    - "focus is all you need loss functions for event based vision"
    - "event based features selection and tracking from intertwined estimation of velocity and generative contours"
    - "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"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"

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)