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: "asynchronous spatial image convolutions for event cameras"
  Abstract: Spatial convolution is arguably the most fundamental of two-dimensional image processing operations. Conventional spatial image convolution can only be applied to a conventional image, that is, an array of pixel values (or similar image representation) that are associated with a single instant in time. Event cameras have serial, asynchronous output with no natural notion of an image frame, and each event arrives with a different timestamp. In this letter, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera. The approach operates on the event stream output of the camera directly without synthesising pseudoimage frames as is common in the literature. The key idea is the introduction of an internal state that directly encodes the convolved image information, which is updated asynchronously as each event arrives from the camera. The state can be read off as often as and whenever required for use in higher level vision algorithms for real-time robotic systems. We demonstrate the application of our method to corner detection, providing an implementation of a Harris corner-response “state” that can be used in real time for feature detection and tracking on robotic systems.

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 "ced color event camera dataset"):
  with members:
    - "events to video bringing modern computer vision to 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"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-3 (induced by source paper "high frame rate video reconstruction based on an event camera"):
  with members:
    - "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"
    - "bringing alive blurred moments"

Connected through hyperedge HE-4 (induced by source paper "fa harris a fast and asynchronous corner detector for event cameras"):
  with members:
    - "semi dense 3d reconstruction with a stereo event camera"
    - "asynchronous photometric feature tracking using events and frames"
    - "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 panoramic tracking for event cameras"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "bringing a blurry frame alive at high frame rate with an event camera"):
  with members:
    - "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"
    - "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)