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: "deep neural networks for improved impromptu trajectory tracking of quadrotors"
  Abstract: Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive "fly-as-you-draw" application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method's potential in real-world applications. Tracking errors are reduced by around 40-50% for both training and testing trajectories from users, highlighting the DNNs' capability of generalizing knowledge.

Connected through hyperedge HE-1 (induced by source paper "catch the ball accurate high speed motions for mobile manipulators via inverse dynamics learning"):
  with members:
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"

Connected through hyperedge HE-2 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "knowledge transfer between robots with similar dynamics for high accuracy impromptu trajectory tracking"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "one shot visual imitation learning via meta learning"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "multi robot transfer learning a dynamical system perspective"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning transferable policies for monocular reactive mav control"
    - "distributed iterative learning control for a team of quadrotors"

Connected through hyperedge HE-4 (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"
    - "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge"):
  with members:
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "learning quadrotor dynamics using neural network for flight control"

Connected through hyperedge HE-6 (induced by source paper "neural learning trajectory tracking control of flexible joint robot manipulators with unknown dynamics"):
  with members:
    - "industrial robot trajectory tracking using multi layer neural networks trained by iterative learning control"
    - "nonlinear systems identification using deep dynamic neural networks"

Connected through hyperedge HE-7 (induced by source paper "inverting learned dynamics models for aggressive multirotor control"):
  with members:
    - "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"

Connected through hyperedge HE-8 (induced by source paper "industrial robot trajectory tracking using multi layer neural networks trained by iterative learning control"):
  with members:
    - "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

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)