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: "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"
  Abstract: In this paper we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queueing network model. Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput. Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. Finally, we validate our theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and network flow models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.

Connected through hyperedge HE-1 (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"
    - "optimal pricing to manage electric vehicles in coupled power and transportation networks"
    - "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"
    - "model predictive control of autonomous mobility on demand systems"

Connected through hyperedge HE-2 (induced by source paper "efficient large scale multi drone delivery using transit networks"):
  with members:
    - "on the interaction between autonomous mobility on demand and public transportation systems"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "route planning in transportation networks"

Connected through hyperedge HE-3 (induced by source paper "on the interaction between autonomous mobility on demand systems and the power network models and coordination algorithms"):
  with members:
    - "optimal pricing to manage electric vehicles in coupled power and transportation networks"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "model predictive control of autonomous mobility on demand systems"

Connected through hyperedge HE-4 (induced by source paper "optimal vehicle dispatching schemes via dynamic pricing"):
  with members:
    - "prices and subsidies in the sharing economy"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "asynchronous methods for deep reinforcement learning"

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)