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: "unsupervised learning of dense optical flow and depth from sparse event data"
  Abstract: In this work we present unsupervised learning of depth and motion from sparse event data generated by a Dynamic Vision Sensor (DVS). To tackle this low level vision task, we use a novel encoder-decoder neural network architecture that aggregates multi-level features and addresses the problem at multiple resolutions. A feature decorrelation technique is introduced to improve the training of the network. A non-local sparse smoothness constraint is used to alleviate the challenge of data sparsity. Our work is the first that generates dense depth and optical flow information from sparse event data. Our results show significant improvements upon previous works that used deep learning for flow estimation from both images and events.

Connected through hyperedge HE-1 (induced by source paper "exploiting event driven cameras for spatio temporal prediction of fast changing trajectories"):
  with members:
    - "continuous time visual inertial odometry for event cameras"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "lstm based network for human gait stability prediction in an intelligent robotic rollator"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "state of the art speech recognition with sequence to sequence models"
    - "ddd17 end to end davis driving dataset"
    - "sequence to sequence video to text"

Connected through hyperedge HE-2 (induced by source paper "network deconvolution"):
  with members:
    - "iterative normalization beyond standardization towards efficient whitening"
    - "on the importance of consistency in training deep neural networks"
    - "improved bilinear pooling with cnns"
    - "fundamental principles of cortical computation unsupervised learning with prediction compression and feedback"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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-4 (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 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"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-5 (induced by source paper "focus is all you need loss functions for event based vision"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "continuous time visual inertial odometry for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "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"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"

Connected through hyperedge HE-6 (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"
    - "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"
    - "slayer spike layer error reassignment in time"

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"
    - "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"
    - "event based vision meets deep learning on steering prediction for self driving cars"

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)