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: "event based 6 dof camera tracking for high speed applications"
  Abstract: In contrast to standard cameras, which produce frames at a fixed rate, event cameras respond asynchronously to pixel-level brightness changes, thus enabling the design of new algorithms for high-speed applications with latencies of microseconds. However, this advantage comes at a cost: because the output is composed by a sequence of events, traditional computer-vision algorithms are not applicable, so that a new paradigm shift is needed. We present an event-based approach for ego-motion estimation, which provides pose updates upon the arrival of each event, thus virtually eliminating latency. Our method is the first work addressing and demonstrating event-based pose tracking in six degrees-of-freedom (DOF) motions in realistic and natural scenes, and it is able to track high-speed motions. The method is successfully evaluated in both indoor and outdoor scenes.

Connected through hyperedge HE-1 (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"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "continuous time visual inertial odometry for event cameras"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "on manifold preintegration for real time visual inertial odometry"
    - "a compact formula for the derivative of a 3 d rotation in exponential coordinates"

Connected through hyperedge HE-3 (induced by source paper "past present and future of simultaneous localization and mapping toward the robust perception age"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "monte carlo localization in hand drawn maps"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)