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: "pointq model of an arterial network calibration and experiments"
  Abstract: The calibration of a PointQ arterial microsimulation model is formulated as a quadratic programming problem (QP) whose decision variables are link flows, demands at entry links, and turn movements at intersections, subject to linear constraints imposed by flow conservation identities and field measurements of a subset of link flows (counts), demands and turn ratios. The quadratic objective function is the deviation of the decision variables from their measured values. The solution to the QP gives estimates of all unmeasured variables and thus yields a fully specified simulation model. Runs of this simulation model can then be compared with other field measurements, such as travel times along routes, to judge the reliability of the calibrated model. A section of the Huntington-Colorado arterial near I-210 in Los Angeles comprising 73 links and 16 intersections is used to illustrate the procedure. Two experiments are conducted with the calibrated model to determine the maximum traffic that can be diverted from the I-210 freeway to the arterial network, with and without permitting changes in the timing plans. The maximum diversion in both cases is obtained by solving a linear programming problem. A third experiment compares the delay and travel time using the existing fixed time control and a max pressure control. The fourth experiment compares two PointQ models: in the first model the freeway traffic follows a pre-specified route while the background traffic moves according to turn ratios, and in the second model turn ratios are modified in a single commodity model to match the link flows. The substantial modification of the turn ratios needed suggests that the use of a single-commodity model as frequently done in CTM models can be misleading...

Connected through hyperedge HE-1 (induced by source paper "integration of information patterns in the modeling and design of mobility management services"):
  with members:
    - "cyber physical control of road freight transport"

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)