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: "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
  Abstract: Event-based cameras are a new passive sensing modality with a number of benefits over traditional cameras, including extremely low latency, asynchronous data acquisition, high dynamic range, and very low power consumption. There has been a lot of recent interest and development in applying algorithms to use the events to perform a variety of three-dimensional perception tasks, such as feature tracking, visual odometry, and stereo depth estimation. However, there currently lacks the wealth of labeled data that exists for traditional cameras to be used for both testing and development. In this letter, we present a large dataset with a synchronized stereo pair event based camera system, carried on a handheld rig, flown by a hexacopter, driven on top of a car, and mounted on a motorcycle, in a variety of different illumination levels and environments. From each camera, we provide the event stream, grayscale images, and inertial measurement unit (IMU) readings. In addition, we utilize a combination of IMU, a rigidly mounted lidar system, indoor and outdoor motion capture, and GPS to provide accurate pose and depth images for each camera at up to 100 Hz. For comparison, we also provide synchronized grayscale images and IMU readings from a frame-based stereo camera system.

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

Connected through hyperedge HE-2 (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"
    - "event based vision a survey"
    - "video to events bringing modern computer vision closer to event cameras"
    - "hats histograms of averaged time surfaces for robust event based object classification"

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"
    - "abmof a novel optical flow algorithm for dynamic vision sensors"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "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 "matching neuromorphic events and color images via adversarial learning"):
  with members:
    - "event based vision a survey"
    - "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"

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 "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-7 (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"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "continuous time intensity estimation using event cameras"

Connected through hyperedge HE-8 (induced by source paper "video to events bringing modern computer vision closer to event cameras"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "ev segnet semantic segmentation for event based cameras"

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)