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: "a provably correct mpc approach to safety control of urban traffic networks"
  Abstract: Model predictive control (MPC) is a popular strategy for urban traffic management that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on finite horizon predictions that are unable to guarantee desirable behaviors over long periods of time. In this paper we design an MPC strategy that is guaranteed to keep the evolution of a network in a desirable yet arbitrary -safe- set, while optimizing a finite horizon cost function. Our approach relies on finding a robust controlled invariant set inside the safe set that provides an appropriate terminal constraint for the MPC optimization problem. An illustrative example is included.

Connected through hyperedge HE-1 (induced by source paper "learning based model predictive control for safe exploration"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"

Connected through hyperedge HE-2 (induced by source paper "a policy search method for temporal logic specified reinforcement learning tasks"):
  with members:
    - "guided policy search as approximate mirror descent"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "concrete problems in ai safety"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "reinforcement learning with temporal logic rewards"
    - "unsupervised perceptual rewards for imitation learning"
    - "path integral guided policy search"

Connected through hyperedge HE-3 (induced by source paper "safety control of monotone systems with bounded uncertainties"):
  with members:
    - "traffic network control from temporal logic specifications"
    - "mixed monotonicity of partial first in first out traffic flow models"

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)