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 "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot"):
  Abstract: Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system's dynamics into account, and optimizes for a given cost function. In practice, however, it often requires an expert to craft and tune this cost function and find trade-offs between different state penalties to satisfy simple high level objectives. In this paper, we use Reinforcement Learning and in particular value learning to approximate the value function given only high level objectives, which can be sparse and binary. Building upon previous works, we present improvements that allowed us to successfully deploy the method on a real world unmanned ground vehicle. Our experiments show that our method can learn the cost function from scratch and without human intervention, while reaching a performance level similar to that of an expert-tuned MPC. We perform a quantitative comparison of these methods with standard MPC approaches both in simulation and on the real robot.

Includes members:
  Member "amz driverless the full autonomous racing system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast and accurate mapping for autonomous racing")
  Member "deep value model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning")
  Member "data driven economic nmpc using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe reinforcement learning using robust mpc")
  Member "plan online learn offline efficient learning and exploration via model based control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural lyapunov model predictive control")
  Member "learning based model predictive control for safe exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploration exploitation in constrained mdps")
  Member "deep reinforcement learning in a handful of trials using probabilistic dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "whole body nonlinear model predictive control through contacts for quadrupeds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion")
  Member "cautious model predictive control using gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning tubes for tube mpc")

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)