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: "sampling based motion planning for active multirotor system identification"
  Abstract: This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.

Connected through hyperedge HE-1 (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"
    - "visual inertial self calibration on informative motion segments"
    - "observability aware trajectory optimization for self calibration with application to uavs"

Connected through hyperedge HE-2 (induced by source paper "build your own visual inertial drone a cost effective and open source autonomous drone"):
  with members:
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"

Connected through hyperedge HE-3 (induced by source paper "history aware autonomous exploration in confined environments using mavs"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-4 (induced by source paper "build your own visual inertial odometry aided cost effective and open source autonomous drone"):
  with members:
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"

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)