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: "model predictive control of autonomous mobility on demand systems"
  Abstract: In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.

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"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"

Connected through hyperedge HE-2 (induced by source paper "a differentially private incentive design for traffic offload to public transportation"):
  with members:
    - "dynamic vehicle redistribution and online price incentives in shared mobility systems"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"

Connected through hyperedge HE-3 (induced by source paper "stochastic model predictive control for autonomous mobility on demand"):
  with members:
    - "routing autonomous vehicles in congested transportation networks structural properties and coordination algorithms"

Connected through hyperedge HE-4 (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"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"

Connected through hyperedge HE-5 (induced by source paper "demand estimation and chance constrained fleet management for ride hailing"):
  with members:
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
    - "data driven robust taxi dispatch under demand uncertainties"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"

Connected through hyperedge HE-6 (induced by source paper "data driven model predictive control of autonomous mobility on demand systems"):
  with members:
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"

Connected through hyperedge HE-7 (induced by source paper "autonomous vehicle routing in congested road networks"):
  with members:
    - "a queueing network approach to the analysis and control of mobility on demand systems"
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"

Connected through hyperedge HE-8 (induced by source paper "predictive positioning and quality of service ridesharing for campus mobility on demand systems"):
  with members:
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"

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)