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 depth and egomotion from sparse event data"
  Abstract: In this work we present a lightweight, unsupervised learning pipeline for \textit{dense} depth, optical flow and egomotion estimation from sparse event output of the Dynamic Vision Sensor (DVS). To tackle this low level vision task, we use a novel encoder-decoder neural network architecture - ECN. #R##N#Our work is the first monocular pipeline that generates dense depth and optical flow from sparse event data only. The network works in self-supervised mode and has just 150k parameters. We evaluate our pipeline on the MVSEC self driving dataset and present results for depth, optical flow and and egomotion estimation. Due to the lightweight design, the inference part of the network runs at 250 FPS on a single GPU, making the pipeline ready for realtime robotics applications. Our experiments demonstrate significant improvements upon previous works that used deep learning on event data, as well as the ability of our pipeline to perform well during both day and night.

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 "eventgan leveraging large scale image datasets for event cameras"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "events to video bringing modern computer vision to event cameras"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "on the variance of the adaptive learning rate and beyond"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"

Connected through hyperedge HE-3 (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 and depth 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-4 (induced by source paper "ev imo motion segmentation dataset and learning pipeline for event cameras"):
  with members:
    - "event based moving object detection and tracking"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "real time panoramic tracking for event cameras"

Connected through hyperedge HE-5 (induced by source paper "motion equivariant networks for event cameras with the temporal normalization transform"):
  with members:
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "ev segnet semantic segmentation for event based cameras"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "polar transformer networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "learning so 3 equivariant representations with spherical cnns"
    - "dynamic steerable blocks in deep residual networks"

Connected through hyperedge HE-6 (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"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "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)