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: "localization for ground robots on manifold representation integration re parameterization and optimization"
  Abstract: In this paper, we focus on localizing ground robots, by probabilistically fusing measurements from the wheel odometry and a monocular camera. For ground robots, the wheel odometry is widely used in localization tasks, especially in applications under planar-scene based environments. However, since the wheel odometry only provides 2D motion estimates, it is extremely challenging to use that for performing accurate full 6D pose (3D position and 3D rotation) estimation. Traditional methods on 6D localization either approximate sensor or motion models, at a cost of accuracy reduction, or rely on other sensors, e.g., inertial measurement unit (IMU), to obtain full 6D motion. By contrast, in this paper, we propose a novel probabilistic framework that is able to use the wheel odometry measurements for high-precision 6D pose estimation, in which only the wheel odometry and a monocular camera are mandatory. Specifically, we propose novel methods for i) formulating a motion manifold by parametric representation, ii) performing manifold based 6D integration with the wheel odometry measurements, and iii) re-parameterizing manifold equations periodically for error reduction. Finally, we propose a complete localization algorithm based on a manifold-assisted sliding-window estimator, fusing measurements from the wheel odometry, a monocular camera, and optionally an IMU. By extensive simulated and real-world experiments, we show that the proposed algorithm outperforms a number of state-of-the-art vision based localization algorithms by a significant margin, especially when deployed in large-scale complicated environments.

Connected through hyperedge HE-1 (induced by source paper "bidirectional trajectory computation for odometer aided visual inertial slam"):
  with members:
    - "visual inertial navigation a concise review"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "visual inertial localization for skid steering robots with kinematic constraints"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "tightly coupled monocular visual odometric slam using wheels and a mems gyroscope"

Connected through hyperedge HE-2 (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"
    - "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"
    - "visual inertial self calibration on informative motion segments"
    - "on manifold preintegration for real time visual inertial 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)