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 online learning approach to model predictive control"):
  Abstract: Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature. This new perspective provides a foundation for leveraging powerful online learning algorithms to design MPC algorithms. Specifically, we propose a new algorithm based on dynamic mirror descent (DMD), an online learning algorithm that is designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), represents a general family of MPC algorithms that includes many existing techniques as special instances. DMD-MPC also provides a fresh perspective on previous heuristics used in MPC and suggests a principled way to design new MPC algorithms. In the experimental section of this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new MPC algorithms on a simple simulated cartpole and a simulated and real-world aggressive driving task. Videos of the real-world experiments can be found at this https URL and this https URL.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "mirror descent search and its acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational inference mpc for bayesian model based reinforcement learning")
  Member "autorally an open platform for aggressive autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning")
  Member "deep reinforcement learning in a handful of trials using probabilistic dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "vision based high speed driving with a deep dynamic observer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end velocity estimation for autonomous racing")
  Member "acceleration of gradient based path integral method for efficient optimal and inverse optimal control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference")
  Member "adaptive online learning in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "risk sensitive path integral control":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning of sequential strategies")

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)