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 "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"):
  Abstract: This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Process (GP) and takes residual model uncertainty into account through a chance constrained formulation. We present a sparse GP approximation with dynamically adjusting inducing inputs, enabling a real-time implementable controller. The formulation is demonstrated in simulations, which show significant improvement with respect to both lap time and constraint satisfaction compared to an NMPC without model learning.

Includes members:
  Member "cautious model predictive control using gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot")
  Member "learning model predictive control for iterative tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks")
  Member "autonomous racing using learning model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extending deep model predictive control with safety augmented value estimation from demonstrations")
  Member "learning model predictive control for iterative tasks a data driven control framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information theoretic model predictive q learning")
  Member "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing the racing line using bayesian optimization")

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.SY(Systems and Control)