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 "deep learning tubes for tube mpc"):
  Abstract: Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning. In order to obtain guarantees of safety in this context, uncertainty must be accurately quantified. This uncertainty may come from errors in learning (due to a lack of data, for example), or may be inherent to the system. Propagating uncertainty in learned dynamics models is a difficult problem. Common approaches rely on restrictive assumptions of how distributions are parameterized or propagated in time. In contrast, in this work we propose using deep learning to obtain expressive and flexible models of how these distributions behave, which we then use for nonlinear Model Predictive Control (MPC). First, we introduce a deep quantile regression framework for control which enforces probabilistic quantile bounds and quantifies epistemic uncertainty. Next, using our method we explore three different approaches for learning tubes which contain the possible trajectories of the system, and demonstrate how to use each of them in a Tube MPC scheme. Furthermore, we prove these schemes are recursively feasible and satisfy constraints with a desired margin of probability. Finally, we present experiments in simulation on a nonlinear quadrotor system, demonstrating the practical efficacy of these ideas.

Includes members:
  Member "regression via arbitrary quantile modeling":
    also connected through other hyperedges:
      (none)
  Member "robust regression for safe exploration in control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "monotonic trends in deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "a general framework for uncertainty estimation in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning resilient behaviors for navigation under uncertainty environments")
  Member "learning control lyapunov functions from counterexamples and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "formal policy learning from demonstrations for reachability properties")
  Member "linear model predictive safety certification for learning based control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample based learning model predictive control for linear uncertain systems")
  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 "cautious model predictive control using gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot")

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)