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: "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
  Abstract: Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guarantee and the cost-effective charging strategy under the real-time electricity pricing. In this paper, we study the joint transportation and charging scheduling for PV systems to balance the transportation and charging demands, ensuring the long-term operation. We adopt a cake cutting game model to capture the interactions among PV groups, the cloud and smart grids. The cloud announces strategies to coordinate the allocation of transportation and energy resources among PV groups. All the PV groups try to maximize their joint transportation and charging utilities. We propose an algorithm to obtain the unique normalized Nash equilibrium point for this problem. Simulations are performed to confirm the effects of our scheme under the real taxi and power grid data sets of New York City. Our results show that our scheme achieves almost the same transportation performance compared with a heuristic scheme, namely, transportation with greedy charging; however, the average energy price of the proposed scheme is 10.86% lower than the latter one.

Connected through hyperedge HE-1 (induced by source paper "coupled charging and driving incentives design for electric vehicles in urban networks"):
  with members:
    - "optimal pricing to manage electric vehicles in coupled power and transportation networks"
    - "electric vehicle charging station placement formulation complexity and solutions"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  with members:
    - "deep reinforcement learning for intelligent transportation systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "sgd learns the conjugate kernel class of the network"
    - "capacity of uav enabled multicast channel joint trajectory design and power allocation"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "deep reinforcement learning for intelligent transportation systems"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for intelligent transportation systems"):
  with members:
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "adam a method for stochastic optimization"

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)