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: "taxi dispatch with real time sensing data in metropolitan areas a receding horizon control approach"
  Abstract: Traditional transportation systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and traffic demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding traffic demand and system status can be collected in real-time. This information provides opportunities to perform various types of control and coordination for large scale intelligent transportation systems. In this paper, we present a novel receding horizon control (RHC) framework to dispatch taxis, which combines highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include reducing taxi idle driving distance and matching spatiotemporal ratio between demand and supply for service quality. Moreover, our RHC framework is compatible with different predictive models and optimization problem formulations. This compatibility property allows us to model disruptive passenger demands and traffic conditions into a robust optimization problem. Extensive trace driven analysis with a real taxi data set from San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the total supply demand ratio error across the city by up to 45%.

Connected through hyperedge HE-1 (induced by source paper "dynamic trip vehicle dispatch with scheduled and on demand requests"):
  with members:
    - "spatio temporal pricing for ridesharing platforms"
    - "flow equilibria via online surge pricing"
    - "optimal vehicle dispatching schemes via dynamic pricing"

Connected through hyperedge HE-2 (induced by source paper "data driven robust taxi dispatch under demand uncertainties"):
  with members:
    - "control of robotic mobility on demand systems a queueing theoretical perspective"

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)