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: "accelerated evaluation of automated vehicles using piecewise mixture models"
  Abstract: The process to certify highly Automated Vehicles has not yet been defined by any country in the world. Currently, companies test Automated Vehicles on public roads, which is time-consuming and inefficient. We proposed the Accelerated Evaluation concept, which uses a modified statistics of the surrounding vehicles and the Importance Sampling theory to reduce the evaluation time by several orders of magnitude, while ensuring the evaluation results are statistically accurate. In this paper, we further improve the accelerated evaluation concept by using Piecewise Mixture Distribution models, instead of Single Parametric Distribution models. We developed and applied this idea to forward collision control system reacting to vehicles making cut-in lane changes. The behavior of the cut-in vehicles was modeled based on more than 403,581 lane changes collected by the University of Michigan Safety Pilot Model Deployment Program. Simulation results confirm that the accuracy and efficiency of the Piecewise Mixture Distribution method outperformed single parametric distribution methods in accuracy and efficiency, and accelerated the evaluation process by almost four orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "an accelerated testing approach for automated vehicles with background traffic described by joint distributions"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "accelerated evaluation of automated vehicles in car following maneuvers"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture models"

Connected through hyperedge HE-2 (induced by source paper "a versatile approach to evaluating and testing automated vehicles based on kernel methods"):
  with members:
    - "an accelerated testing approach for automated vehicles with background traffic described by joint distributions"
    - "trafficnet an open naturalistic driving scenario library"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

Connected through hyperedge HE-3 (induced by source paper "sequential experimentation to efficiently test automated vehicles"):
  with members:
    - "towards affordable on track testing for autonomous vehicle a kriging based statistical approach"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture models"

Connected through hyperedge HE-4 (induced by source paper "towards secure and safe appified automated vehicles"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

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)