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: "graph based object classification for neuromorphic vision sensing"
  Abstract: Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, object classification with NVS streams cannot leverage on state-of-the-art convolutional neural networks (CNNs), since NVS does not produce frame representations. To circumvent this mismatch between sensing and processing with CNNs, we propose a compact graph representation for NVS. We couple this with novel residual graph CNN architectures and show that, when trained on spatio-temporal NVS data for object classification, such residual graph CNNs preserve the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we present and make available a 100k dataset of NVS recordings of the American sign language letters, acquired with an iniLabs DAVIS240c device under real-world conditions.

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"
    - "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"
    - "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"
    - "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"
    - "event based star tracking via multiresolution progressive hough transforms"

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"
    - "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"
    - "hats histograms of averaged time surfaces for robust event based object classification"

Connected through hyperedge HE-4 (induced by source paper "point gnn graph neural network for 3d object detection in a point cloud"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "fast point r cnn"
    - "a comprehensive survey on graph neural networks"
    - "pointpillars fast encoders for object detection from point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-5 (induced by source paper "graph based spatial temporal feature learning for neuromorphic vision sensing"):
  with members:
    - "spatiotemporal filtering for event based action recognition"
    - "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"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "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.CV(Computer Vision and Pattern Recognition)