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: "two layer model predictive battery thermal and energy management optimization for connected and automated electric vehicles"
  Abstract: Future vehicles are expected to be able to exploit increasingly the connected driving environment for efficient, comfortable, and safe driving. Given relatively slow dynamics associated with the state of charge and temperature response in electrified vehicles with large batteries, a long prediction/planning horizon is needed to achieve improved energy efficiency benefits. In this paper, we develop a two-layer Model Predictive Control (MPC) strategy for battery thermal and energy management of electric vehicle (EV), aiming at improving fuel economy through real-time prediction and optimization. In the first layer, the long-term traffic flow information and an approximate model reflective of the relatively slow battery temperature dynamics are leveraged to minimize energy consumption required for battery cooling while maintaining the battery temperature within the desired operating range. In the second layer, the scheduled battery thermal and state of charge (SOC) trajectories planned to achieve long-term battery energy-optimal thermal behavior are used as the reference over a short horizon to regulate the battery temperature. Additionally, an intelligent online constraint handling (IOCH) algorithm is developed to compensate for the mismatch between the actual and predicted driving conditions and reduce the chance for battery temperature constraint violation. The simulation results show that, depending on the driving cycle, the proposed two-layer MPC is able to save 2.8-7.9% of the battery energy compared to the traditional rule-based controller in connected and automated vehicle (CAV) operation scenario. Moreover, as compared to a single layer MPC with a long horizon, the two-layer structure of the proposed MPC solution reduces significantly the computing effort without compromising the performance.

Connected through hyperedge HE-1 (induced by source paper "thermal responses of connected hevs engine and aftertreatment systems to eco driving"):
  with members:
    - "sequential optimization of speed thermal load and power split in connected hevs"
    - "integrated optimization of power split engine thermal management and cabin heating for hybrid electric vehicles"
    - "control of connected and automated vehicles state of the art and future challenges"

Connected through hyperedge HE-2 (induced by source paper "sequential optimization of speed thermal load and power split in connected hevs"):
  with members:
    - "control of connected and automated vehicles state of the art and future challenges"
    - "model predictive climate control of connected and automated vehicles for improved energy efficiency"

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)