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: "optimal pricing to manage electric vehicles in coupled power and transportation networks"
  Abstract: We study the system-level effects of the introduction of large populations of Electric Vehicles (EVs) on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an “extended” transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.

Connected through hyperedge HE-1 (induced by source paper "coupled charging and driving incentives design for electric vehicles in urban networks"):
  with members:
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "electric vehicle charging station placement formulation complexity and solutions"

Connected through hyperedge HE-2 (induced by source paper "on the interaction between autonomous mobility on demand systems and power distribution networks an optimal power flow approach"):
  with members:
    - "on the interaction between autonomous mobility on demand systems and the power network models and coordination algorithms"
    - "tools for improving resilience of electric distribution systems with networked microgrids"
    - "data driven model predictive control of autonomous mobility on demand systems"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"
    - "model predictive control of autonomous mobility on demand systems"

Connected through hyperedge HE-3 (induced by source paper "investment in ev charging spots for parking"):
  with members:
    - "the cost of free spectrum"
    - "distributed demand response and user adaptation in smart grids"

Connected through hyperedge HE-4 (induced by source paper "smart routing of electric vehicles for load balancing in smart grids"):
  with members:
    - "fragility of the commons under prospect theoretic risk attitudes"
    - "stochastic games for smart grid energy management with prospect prosumers"
    - "load shifting in the smart grid to participate or not"
    - "towards a consumer centric grid a behavioral perspective"
    - "charging games in networks of electrical vehicles"
    - "playing the wrong game smoothness bounds for congestion games with behavioral biases"

Connected through hyperedge HE-5 (induced by source paper "on the interaction between autonomous mobility on demand systems and the power network models and coordination algorithms"):
  with members:
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"
    - "model predictive control of autonomous mobility on demand systems"

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)