human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning based model predictive control for safe exploration"
  Abstract: Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that can provide provable high-probability safety guarantees. To this end, we exploit regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we do not assume that model uncertainties are independent. Based on these predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. In our experiments, we show that the resulting algorithm can be used to safely and efficiently explore and learn about dynamic systems.

Connected through hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-2 (induced by source paper "constrained upper confidence reinforcement learning"):
  with members:
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "a lyapunov based approach to safe reinforcement learning"
    - "ai safety gridworlds"
    - "budget constrained multi armed bandits with multiple plays"
    - "constrained policy optimization"
    - "fairness in learning classic and contextual bandits"
    - "concrete problems in ai safety"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "deep value model predictive control"
    - "data driven economic nmpc using reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "cautious model predictive control using gaussian process regression"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "deep learning tubes for tube mpc"):
  with members:
    - "regression via arbitrary quantile modeling"
    - "robust regression for safe exploration in control"
    - "monotonic trends in deep neural networks"
    - "a general framework for uncertainty estimation in deep learning"
    - "learning control lyapunov functions from counterexamples and demonstrations"
    - "linear model predictive safety certification for learning based control"
    - "cautious model predictive control using gaussian process regression"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-5 (induced by source paper "safe linear thompson sampling with side information"):
  with members:
    - "linear stochastic bandits under safety constraints"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "being optimistic to be conservative quickly learning a cvar policy"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "policy certificates towards accountable reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "the potential of the return distribution for exploration in rl"
    - "automated curriculum learning for neural networks"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"

Connected through hyperedge HE-7 (induced by source paper "sample based learning model predictive control for linear uncertain systems"):
  with members:
    - "learning how to autonomously race a car a predictive control approach"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "linear model predictive safety certification for learning based control"
    - "learning based predictive control for linear systems a unitary approach"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "real time control for autonomous racing based on viability theory"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-8 (induced by source paper "low level control of a quadrotor with deep model based reinforcement learning"):
  with members:
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to adapt meta learning for model based control"
    - "control of a quadrotor with reinforcement learning"
    - "swish a self gated activation function"
    - "learning quadrotor dynamics using neural network for flight control"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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.SY(Systems and Control)