human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "aggressive online control of a quadrotor via deep network representations of optimality principles"):
  Abstract: Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the large computational requirements of current state of the art implementations. In this work, we make use of a deep neural network to directly map the robot states to control actions. The network is trained offline to imitate the optimal control computed by a time consuming direct nonlinear method. A mixture of time optimality and power optimality is considered with a continuation parameter used to select the predominance of each objective. We apply our networks (termed G\&CNets) to aggressive quadrotor control, first in simulation and then in the real world. We give insight into the factors that influence the `reality gap' between the quadrotor model used by the offline optimal control method and the real quadrotor. Furthermore, we explain how we set up the model and the control structure on-board of the real quadrotor to successfully close this gap and perform time-optimal maneuvers in the real world. Finally, G\&CNet's performance is compared to state-of-the-art differential-flatness-based optimal control methods. We show, in the experiments, that G\&CNets lead to significantly faster trajectory execution due to, in part, the less restrictive nature of the allowed state-to-input mappings.

Includes members:
  Member "learning the optimal state feedback via supervised imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverting learned dynamics models for aggressive multirotor control")
  Member "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "technical report safe aggressive quadrotor flight via reachability based trajectory design")
  Member "on the stability analysis of deep neural network representations of an optimal state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "control of a quadrotor with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "real time optimal control via deep neural networks study on landing problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "trajectory generation for quadrotor based systems using numerical optimal control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonlinear model predictive control with actuator constraints for multi rotor aerial vehicles")
  Member "deep neural networks for improved impromptu trajectory tracking of quadrotors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "catch the ball accurate high speed motions for mobile manipulators via inverse dynamics learning")

Question: Please tell me which class the source paper 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)