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: "steering a predator robot using a mixed frame event driven convolutional neural network"
  Abstract: This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.

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 "motion equivariance of event based camera data with the temporal normalization transform"):
  with members:
    - "motion equivariant networks for event cameras with the temporal normalization transform"
    - "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"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "event based vision meets deep learning on steering prediction for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "dashnet a hybrid artificial and spiking neural network for high speed object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "training deep neural networks for binary communication with the whetstone method"
    - "pred18 dataset and further experiments with davis event camera in predator prey robot chasing"
    - "direct training for spiking neural networks faster larger better"
    - "context aware synthesis for video frame interpolation"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-4 (induced by source paper "motion equivariant networks for event cameras with the temporal normalization transform"):
  with members:
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "polar transformer networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-5 (induced by source paper "closing the accuracy gap in an event based visual recognition task"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "fast and efficient asynchronous neural computation with adapting spiking neural networks"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-6 (induced by source paper "learn to see by events rgb frame synthesis from 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"
    - "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"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"

Connected through hyperedge HE-7 (induced by source paper "unsupervised event based learning of optical flow depth and egomotion"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "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"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "recurrent neural network for learning densedepth and ego motion from video"

Connected through hyperedge HE-8 (induced by source paper "event based vision meets deep learning on steering prediction for self driving cars"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "ddd17 end to end davis driving dataset"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"
    - "hfirst a temporal approach to object recognition"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "imagenet large scale visual recognition challenge"

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.RO(Robotics)