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: "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
  Abstract: This paper presents a learning-based approach for impromptu trajectory tracking of non-minimum phase systems -- systems with unstable inverse dynamics. In the control systems literature, inversion-based feedforward approaches are commonly used for improving the trajectory tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to the inherent instability. In order to resolve the instability issue, they assume that models of the systems are known and have dealt with the non-minimum phase systems by pre-actuation or inverse approximation techniques. In this work, we extend our deep-neural-network-enhanced impromptu trajectory tracking approach to the challenging case of non-minimum phase systems. Through theoretical discussions, simulations, and experiments, we show the stability and effectiveness of our proposed learning approach. In fact, for a known system, our approach performs equally well or better as a typical model-based approach but does not require a prior model of the system. Interestingly, our approach also shows that including more information in training (as is commonly assumed to be useful) does not lead to better performance but may trigger instability issues and impede the effectiveness of the overall approach.

Connected through hyperedge HE-1 (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"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "learning quadrotor dynamics using neural network for flight control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-2 (induced by source paper "industrial robot trajectory tracking using multi layer neural networks trained by iterative learning control"):
  with members:
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "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)