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: "observability aware trajectory optimization for self calibration with application to uavs"
  Abstract: We study the nonlinear observability of a systems states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Common trajectory-planning algorithms tend to generate motions that lead to an unobservable subspace of the system state, causing suboptimal state estimation. We address this problem with a method that reasons about the quality of observability while respecting system dynamics and motion constraints to yield the optimal trajectory for rapid convergence of the self-calibration states (or other user-chosen states). Experiments performed on a simulated quadrotor system with a GPS-IMU sensor suite demonstrate the benefits of the optimized observability-aware trajectories when compared to a covariance-based approach and multiple heuristic approaches. Our method is approx. 80x faster than the covariance-based approach and achieves better results than any other approach in the self-calibration task. We applied our method to a waypoint navigation task and achieved a approx. 2x improvement in the integrated RMSE of the global position estimates and approx. 4x improvement in the integrated RMSE of the GPS-IMU transformation estimates compared to a minimal-energy trajectory planner.

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"
    - "sampling based motion planning for active multirotor system identification"

Connected through hyperedge HE-2 (induced by source paper "sensor assignment algorithms to improve observability while tracking targets"):
  with members:
    - "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals"
    - "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals"
    - "distributed state estimation using intermittently connected robot networks"
    - "active target tracking with self triggered communications in multi robot teams"
    - "distributed algorithms for feature extraction off loading in multi camera visual sensor networks"
    - "scheduling nonlinear sensors for stochastic process estimation"

Connected through hyperedge HE-3 (induced by source paper "sampling based motion planning for active multirotor system identification"):
  with members:
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "trajectory synthesis for fisher information maximization"

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)