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: "hats histograms of averaged time surfaces for robust event based object classification"
  Abstract: Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.

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 "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"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of object motion estimation from retinal events for event based object tracking"):
  with members:
    - "event based vision a survey"
    - "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"
    - "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-5 (induced by source paper "effective aer object classification using segmented probability maximization learning in spiking neural networks"):
  with members:
    - "unsupervised aer object recognition based on multiscale spatio temporal features and spiking neurons"
    - "hfirst a temporal approach to object recognition"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"

Connected through hyperedge HE-6 (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-7 (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"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-8 (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"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "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"

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)