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 "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.

Includes members:
  Member "design of deep neural networks as add on blocks for improving impromptu trajectory tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "catch the ball accurate high speed motions for mobile manipulators via inverse dynamics learning")
  Member "deep neural networks for improved impromptu trajectory tracking of quadrotors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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)