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: "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
  Abstract: In this paper, we prove that the dynamical model of a quadrotor subject to linear rotor drag effects is differentially flat in its position and heading. We use this property to compute feed-forward control terms directly from a reference trajectory to be tracked. The obtained feed-forward terms are then used in a cascaded, nonlinear feedback control law that enables accurate agile flight with quadrotors. Compared to state-of-the-art control methods, which treat the rotor drag as an unknown disturbance, our method reduces the trajectory tracking error significantly. Finally, we present a method based on a gradient-free optimization to identify the rotor drag coefficients, which are required to compute the feed-forward control terms. The new theoretical results are thoroughly validated trough extensive comparative experiments.

Connected through hyperedge HE-1 (induced by source paper "aggressive online control of a quadrotor via deep network representations of optimality principles"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "control of a quadrotor with reinforcement learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "trajectory generation for quadrotor based systems using numerical optimal control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-2 (induced by source paper "inverting learned dynamics models for aggressive multirotor control"):
  with members:
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

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)