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: "bioinspired visual motion estimation"
  Abstract: Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. Twenty five years ago, in this very journal, Carver Mead outlined his argument for replicating biological processing in silicon circuits. His vision served as the foundation for the field of neuromorphic engineering, which has experienced a rapid growth in interest over recent years as the ideas and technologies mature. Replicating biological visual sensing was one of the first tasks attempted in the neuromorphic field. In this paper, we focus specifically on the task of visual motion estimation. We describe the task itself, present the progression of works from the early first attempts through to the modern day state-of-the-art, and provide an outlook for future directions in the field.

Connected through hyperedge HE-1 (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"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-2 (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"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "unsupervised learning of dense optical flow depth and egomotion from sparse event data"):
  with members:
    - "evenly cascaded convolutional networks"
    - "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"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-5 (induced by source paper "ev flownet self supervised optical flow estimation for event based cameras"):
  with members:
    - "abmof a novel optical flow algorithm for dynamic vision sensors"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "guided optical flow learning"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "stacked hourglass networks for human pose estimation"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "spiking optical flow for event based sensors using ibm s truenorth neurosynaptic system"):
  with members:
    - "deep neural networks are robust to weight binarization and other non linear distortions"

Connected through hyperedge HE-7 (induced by source paper "hfirst a temporal approach to object recognition"):
  with members:
    - "fast neuromimetic object recognition using fpga outperforms gpu implementations"

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)