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: "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"
  Abstract: Autonomous vehicles will benefit from the ability to perform aggressive driving maneuvers in safety-critical situations where the full use of available tire-road friction is required. Unfortunately, vehicle steering dynamics become highly nonlinear and difficult to model near the limits of tire adhesion, making accurate control of these maneuvers difficult. One promising approach is to use iterative learning control (ILC) as a method of gradually determining the proper steering input for a transient driving maneuver by repeating the maneuver several times and using information from previous iterations to improve the reference tracking performance. This paper explores the viability of this concept by applying learning algorithms for multiple-lap path tracking of an autonomous race vehicle. Racing is an ideal scenario for iterative learning control because race cars drive the same sequence of turns while operating near the physical limits of tire-road friction. This creates a repeatable set of nonlinear vehicle dynamics and road conditions from lap to lap. Simulation results are used to design and test convergence of both a proportional-derivative (PD) and quadratically optimal (Q-ILC) iterative learning controller, and experimental results are presented on an Audi TTS race vehicle driving several laps around Thunderhill Raceway in Willows, CA at combined vehicle accelerations of up to 8 m/s2. Both control algorithms are able to correct transient path tracking errors and significantly improve the performance provided by a reference feedforward controller.

Connected through hyperedge HE-1 (induced by source paper "computing the racing line using bayesian optimization"):
  with members:
    - "botorch programmable bayesian optimization in pytorch"
    - "learning how to autonomously race a car a predictive control approach"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "learning how to autonomously race a car a predictive control approach"):
  with members:
    - "real time control for autonomous racing based on viability theory"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "autonomous racing using learning model predictive control"

Connected through hyperedge HE-3 (induced by source paper "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"):
  with members:
    - "cautious model predictive control using gaussian process regression"
    - "learning model predictive control for iterative tasks"
    - "autonomous racing using learning model predictive control"
    - "learning model predictive control for iterative tasks a data driven control framework"

Connected through hyperedge HE-4 (induced by source paper "autonomous racing using learning model predictive control"):
  with members:
    - "learning model predictive control for iterative tasks"

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.RO(Robotics)