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: "design and implementation of ecological adaptive cruise control for autonomous driving with communication to traffic lights"
  Abstract: This paper presents the design and implementation results of an ecological adaptive cruise controller (ECO-ACC) which exploits driving automation and connectivity. The controller avoids front collisions and traffic light violations, and is designed to reduce the energy consumption of connected automated vehicles by utilizing historical and real-time signal phase and timing data of traffic lights that adapt to the current traffic conditions. We propose an optimization-based generation of a reference velocity, and a velocity-tracking model predictive controller that avoids front collisions and violations. We present an experimental setup encompassing the real vehicle and controller in the loop, and an environment simulator in which the traffic flow and the traffic light patterns are calibrated on real-world data. We present and analyze simulation and experimental results, finding a significant potential for energy consumption reduction, even in the presence of traffic.

Connected through hyperedge HE-1 (induced by source paper "cooperation aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network"):
  with members:
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "real time ecological velocity planning for plug in hybrid vehicles with partial communication to traffic lights"
    - "safe reinforcement learning on autonomous vehicles"
    - "driving in dense traffic with model free reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-2 (induced by source paper "real time ecological velocity planning for plug in hybrid vehicles with partial communication to traffic lights"):
  with members:
    - "hardware in the loop for connected automated vehicles testing in real traffic"
    - "control of connected and automated vehicles state of the art and future challenges"

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)