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: "real time intensity image reconstruction for event cameras using manifold regularisation"
  Abstract: Event cameras or neuromorphic cameras mimic the human perception system as they measure the per-pixel intensity change rather than the actual intensity level. In contrast to traditional cameras, such cameras capture new information about the scene at MHz frequency in the form of sparse events. The high temporal resolution comes at the cost of losing the familiar per-pixel intensity information. In this work we propose a variational model that accurately models the behaviour of event cameras, enabling reconstruction of intensity images with arbitrary frame rate in real-time. Our method is formulated on a per-event-basis, where we explicitly incorporate information about the asynchronous nature of events via an event manifold induced by the relative timestamps of events. In our experiments we verify that solving the variational model on the manifold produces high-quality images without explicitly estimating optical flow.

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 "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 "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"
    - "continuous time intensity estimation using event cameras"

Connected through hyperedge HE-4 (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"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learn to see by events color frame synthesis from event and rgb cameras"):
  with members:
    - "events to video bringing modern computer vision to 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"
    - "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"
    - "continuous time intensity estimation using event cameras"

Connected through hyperedge HE-6 (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"
    - "bringing alive blurred moments"

Connected through hyperedge HE-7 (induced by source paper "photorealistic image reconstruction from hybrid intensity and event based sensor"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "independent motion detection with event driven cameras"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "unsupervised learning of depth and ego motion from video"
    - "real time panoramic tracking for event cameras"
    - "demon depth and motion network for learning monocular stereo"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-8 (induced by source paper "continuous time intensity estimation using event cameras"):
  with members:
    - "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"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"
    - "asynchronous photometric feature tracking using events and frames"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "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"

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)