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: "event based vision a survey"
  Abstract: Event cameras are bio-inspired sensors that work radically different from traditional cameras. Instead of capturing images at a fixed rate, they measure per-pixel brightness changes asynchronously. This results in a stream of events, which encode the time, location and sign of the brightness changes. Event cameras posses outstanding properties compared to traditional cameras: very high dynamic range (140 dB vs. 60 dB), high temporal resolution (in the order of microseconds), low power consumption, and do not suffer from motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as high speed and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

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"
    - "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"
    - "attention mechanisms for object recognition with event based cameras"

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 star tracking via multiresolution progressive hough transforms"

Connected through hyperedge HE-3 (induced by source paper "airsim drone racing lab"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-4 (induced by source paper "event based angular velocity regression with spiking networks"):
  with members:
    - "deep learning in spiking neural networks"
    - "end to end learning of representations for asynchronous event based data"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"

Connected through hyperedge HE-5 (induced by source paper "a tracking system for baseball game reconstruction"):
  with members:
    - "the world of fast moving objects"
    - "learning to detect human object interactions"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "rmpe regional multi person pose estimation"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "recurrent human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "a large scale event based detection dataset for automotive"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "edge detection for event cameras using intra pixel area events"
    - "ced color event camera dataset"
    - "speed invariant time surface for learning to detect corner points with event based cameras"
    - "video to events bringing modern computer vision closer to event cameras"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "end to end learning of object motion estimation from retinal events for event based object tracking"):
  with members:
    - "atom accurate tracking by overlap maximization"
    - "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 event based learning of optical flow depth and egomotion"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "real time clustering and multi target tracking using event based sensors"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"

Connected through hyperedge HE-8 (induced by source paper "matching neuromorphic events and color images via adversarial learning"):
  with members:
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "rift multi modal image matching based on radiation invariant feature transform"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "sift meets cnn a decade survey of instance retrieval"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"
    - "understanding deep image representations by inverting them"

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)