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: "visual inertial self calibration on informative motion segments"
  Abstract: Environmental conditions and external effects, such as shocks, have a significant impact on the calibration parameters of visual-inertial sensor systems. Thus long-term operation of these systems cannot fully rely on factory calibration. Since the observability of certain parameters is highly dependent on the motion of the device, using short data segments at device initialization may yield poor results. When such systems are additionally subject to energy constraints, it is also infeasible to use full-batch approaches on a big dataset and careful selection of the data is of high importance. In this paper, we present a novel approach for resource efficient self-calibration of visual-inertial sensor systems. This is achieved by casting the calibration as a segment-based optimization problem that can be run on a small subset of informative segments. Consequently, the computational burden is limited as only a predefined number of segments is used. We also propose an efficient information-theoretic selection to identify such informative motion segments. In evaluations on a challenging dataset, we show our approach to significantly outperform state-of-the-art in terms of computational burden while maintaining a comparable accuracy.

Connected through hyperedge HE-1 (induced by source paper "localization for ground robots on manifold representation integration re parameterization and optimization"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "robocentric visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "tightly coupled monocular visual odometric slam using wheels and a mems gyroscope"
    - "oxiod the dataset for deep inertial odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"

Connected through hyperedge HE-2 (induced by source paper "experimental comparison of visual aided odometry methods for rail vehicles"):
  with members:
    - "appearance based landmark selection for efficient long term visual localization"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust odometry using sensor consensus analysis"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-3 (induced by source paper "a lightweight and accurate localization algorithm using multiple inertial measurement units"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "localization for ground robots on manifold representation integration re parameterization and optimization"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "observability aware self calibration of visual and inertial sensors for ego motion estimation"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "sampling based motion planning for active multirotor system identification"
    - "observability aware trajectory optimization for self calibration with application to uavs"

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)