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 "variable scaling rate control for collision free teleoperation of an unmanned aerial vehicle"):
  Abstract: We propose that automatically adjusting the scale factor in rate-control teleoperation could enable a human operator to better control the motion of a remote robot. In this paper, we present four new variable-scaling rate-control methods that adjust the scale factor depending on the state of the user's input commands and/or the risk of a collision between the robot and its environment. Methods that depend on the risk of a collision are designed to guarantee collision avoidance by setting the scale factor to be zero if the operator issues a command that would result in a collision between the robot and its environment. A within-subject user study was conducted to determine the effects of the four newly designed rate-control methods and a traditional fixed-scale rate-control method on a person's ability to complete a navigation task in a simulated two-dimensional environment. The results of this study indicate that well-designed variable-scale rate control can guarantee collision-free teleoperation without reducing task efficiency.

Includes members:
  Member "shared autonomy via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real world human robot collaborative reinforcement learning")
  Member "autonomy infused teleoperation with application to bci manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling")
  Member "shared autonomy via hindsight optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections")

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)