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: "model predictive control based trajectory generation for autonomous vehicles an architectural approach"
  Abstract: Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path-or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.

Connected through hyperedge HE-1 (induced by source paper "a probabilistic framework for imitating human race driver behavior"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "imitating driver behavior with generative adversarial networks"
    - "generative adversarial imitation learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "a system s perspective towards an architecture framework for safe automated vehicles"):
  with members:
    - "towards a skill and ability based development process for self aware automated road vehicles"
    - "hazard analysis and risk assessment for an automated unmanned protective vehicle"

Connected through hyperedge HE-3 (induced by source paper "investigating functional redundancies in the context of vehicle automation a trajectory tracking perspective"):
  with members:
    - "hazard analysis and risk assessment for an automated unmanned protective vehicle"

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)