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: "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"
  Abstract: While autonomous multirotor micro aerial vehicles (MAVs) are uniquely well suited for certain types of missions benefiting from stationary flight capabilities, their more widespread usage still faces many hurdles, due in particular to their limited range and the difficulty of fully automating their deployment and retrieval. In this paper we address these issues by solving the problem of the automated landing of a quadcopter on a ground vehicle moving at relatively high speed. We present our system architecture, including the structure of our Kalman filter for the estimation of the relative position and velocity between the quadcopter and the landing pad, as well as our controller design for the full rendezvous and landing maneuvers. The system is experimentally validated by successfully landing in multiple trials a commercial quadcopter on the roof of a car moving at speeds of up to 50 km/h.

Connected through hyperedge HE-1 (induced by source paper "long duration fully autonomous operation of rotorcraft unmanned aerial systems for remote sensing data acquisition"):
  with members:
    - "long duration autonomy for small rotorcraft uas including recharging"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"

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:
    - "sampling based motion planning for active multirotor system identification"
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"

Connected through hyperedge HE-3 (induced by source paper "build your own visual inertial odometry aided cost effective and open source autonomous drone"):
  with members:
    - "sampling based motion planning for active multirotor system identification"
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "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)