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: "continuous time visual inertial odometry for event cameras"
  Abstract: Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. Recent work has shown that a continuous-time representation of the event camera pose can deal with the high temporal resolution and asynchronous nature of this sensor in a principled way. In this paper, we leverage such a continuous-time representation to perform visual-inertial odometry with an event camera. This representation allows direct integration of the asynchronous events with microsecond accuracy and the inertial measurements at high frequency. The event camera trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines. This formulation significantly reduces the number of variables in trajectory estimation problems. We evaluate our method on real data from several scenes and compare the results against ground truth from a motion-capture system. We show that our method provides improved accuracy over the result of a state-of-the-art visual odometry method for event cameras. We also show that both the map orientation and scale can be recovered accurately by fusing events and inertial data. To the best of our knowledge, this is the first work on visual-inertial fusion with event cameras using a continuous-time framework.

Connected through hyperedge HE-1 (induced by source paper "exploiting event driven cameras for spatio temporal prediction of fast changing trajectories"):
  with members:
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "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 "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-3 (induced by source paper "visual inertial navigation a concise review"):
  with members:
    - "observability aware self calibration of visual and inertial sensors for ego motion estimation"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "visual inertial object detection and mapping"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "closed form solution to cooperative visual inertial structure from motion"
    - "robocentric visual inertial odometry"
    - "relocalization global optimization and map merging for monocular visual inertial slam"

Connected through hyperedge HE-4 (induced by source paper "ced color event camera dataset"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "asynchronous spatial image convolutions 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"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

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"
    - "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"
    - "unsupervised event based learning of optical flow depth and egomotion"

Connected through hyperedge HE-6 (induced by source paper "efficient derivative computation for cumulative b splines on lie groups"):
  with members:
    - "trajectory representation and landmark projection for continuous time structure from motion"
    - "spline error weighting for robust visual inertial fusion"
    - "a micro lie theory for state estimation in robotics"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"

Connected through hyperedge HE-7 (induced by source paper "neuromorphic visual odometry system for intelligent vehicle application with bio inspired vision sensor"):
  with members:
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "semi dense 3d reconstruction with a stereo event camera"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "asynchronous photometric feature tracking using events and frames"
    - "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"
    - "direct sparse odometry"

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.RO(Robotics)