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: "ev flownet self supervised optical flow estimation for event based cameras"
  Abstract: Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.

Connected through hyperedge HE-1 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "ced color event camera dataset"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "pytorch an imperative style high performance deep learning library"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-2 (induced by source paper "event probability mask epm and event denoising convolutional neuralnetwork edncnn for neuromorphic cameras"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "ced color event camera dataset"
    - "event based feature extraction using adaptive selection thresholds"
    - "evdodgenet deep dynamic obstacle dodging with event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"

Connected through hyperedge HE-3 (induced by source paper "spike flownet event based optical flow estimation with energy efficient hybrid neural networks"):
  with members:
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "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"
    - "abmof a novel optical flow algorithm for dynamic vision sensors"
    - "spiking optical flow for event based sensors using ibm s truenorth neurosynaptic system"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-4 (induced by source paper "globally optimal contrast maximisation for event based motion estimation"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "event based star tracking via multiresolution progressive hough transforms"
    - "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"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "star tracking using an event camera"
    - "eventnet asynchronous recursive event processing"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"

Connected through hyperedge HE-5 (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-6 (induced by source paper "graph based spatial temporal feature learning for neuromorphic vision sensing"):
  with members:
    - "spatiotemporal filtering for event based action recognition"
    - "graph based object classification for neuromorphic vision sensing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "attention mechanisms for object recognition with event based cameras"
    - "local spectral graph convolution for point set feature learning"
    - "how powerful are graph neural networks"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-7 (induced by source paper "graph based object classification for neuromorphic vision sensing"):
  with members:
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "attention mechanisms for object recognition with event based cameras"
    - "local spectral graph convolution for point set feature learning"
    - "how powerful are graph neural networks"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-8 (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"

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)