human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the interaction between autonomous mobility on demand systems and power distribution networks an optimal power flow approach"):
  Abstract: In future transportation systems, the charging behavior of electric Autonomous Mobility on Demand (AMoD) fleets, i.e., fleets of self-driving cars that service on-demand trip requests, will likely challenge power distribution networks (PDNs), causing overloads or voltage drops. In this paper, we show that these challenges can be significantly attenuated if the PDNs' operational constraints and exogenous loads (e.g., from homes or businesses) are considered when operating the electric AMoD fleet. We focus on a system-level perspective, assuming full cooperation between the AMoD and the PDN operators. Through this single entity perspective, we derive an upper bound on the benefits of coordination. We present an optimization-based modeling approach to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints. For a case study in Orange County, CA, we show that coordinating the electric AMoD fleet and the PDNs helps to reduce 99% of overloads and 50% of voltage drops which the electric AMoD fleet causes without coordination. Our results show that coordinating electric AMoD and PDNs helps to level loads and can significantly postpone the point at which upgrading the network's capacity to a larger scale becomes inevitable to preserve stability.

Includes members:
  Member "on the interaction between autonomous mobility on demand systems and the power network models and coordination algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the interaction between autonomous mobility on demand and public transportation systems")
  Member "optimal pricing to manage electric vehicles in coupled power and transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coupled charging and driving incentives design for electric vehicles in urban networks")
  Member "tools for improving resilience of electric distribution systems with networked microgrids":
    also connected through other hyperedges:
      (none)
  Member "data driven model predictive control of autonomous mobility on demand systems":
    also connected through other hyperedges:
      (none)
  Member "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an unsupervised approach to geographical knowledge discovery using street level and street network images")
  Member "a bcmp network approach to modeling and controlling autonomous mobility on demand systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient large scale multi drone delivery using transit networks")
  Member "model predictive control of autonomous mobility on demand systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a differentially private incentive design for traffic offload to public transportation")

Question: Please tell me which class the source paper 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)