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: "spiking optical flow for event based sensors using ibm s truenorth neurosynaptic system"
  Abstract: This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on TrueNorth with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity. The spiking neural network is a variant of the Barlow Levick method for optical flow estimation. The system is evaluated on two recordings for which ground truth motion is available, and achieves an Average Endpoint Error of 11% at an estimated power budget of under 80mW for the sensor and computation.

Connected through hyperedge HE-1 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "snn under attack are spiking deep belief networks vulnerable to adversarial examples"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "adversarial patch"
    - "direct training for spiking neural networks faster larger better"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-2 (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"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-3 (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-4 (induced by source paper "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"):
  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"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "vertical landing for micro air vehicles using event based optical flow"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "abmof a novel optical flow algorithm for dynamic vision sensors"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-5 (induced by source paper "inceptive event time surfaces for object classification using neuromorphic cameras"):
  with members:
    - "event based moving object detection and tracking"
    - "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"
    - "hfirst a temporal approach to object recognition"
    - "going deeper with convolutions"

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)