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 node models for high dimensional road networks"):
  Abstract: Macroscopic traffic models are necessary for simulation and study of traffic’s complex macro-scale dynamics, and are often used by practitioners for road network planning, integrated corridor management, and other applications. These models have two parts: a link model, which describes traffic flow behavior on individual roads, and a node model, which describes behavior at road junctions. As the road networks under study become larger and more complex — nowadays often including arterial networks — the node model becomes more important. Despite their great importance to macroscopic models, however, only recently have node models had similar levels of attention as link models in the literature. This paper focuses on the first order node model and has two main contributions. First, we formalize the multi-commodity flow distribution at a junction as an optimization problem with all the necessary constraints. Most interesting here is the formalization of input flow priorities. Then, we discuss a very common “conservation of turning fractions” or “first-in-first-out” (FIFO) constraint, and how it often produces unrealistic spillback. This spillback occurs when, at a diverge, a queue develops for a movement that only a few lanes service, but FIFO requires that all lanes experience spillback from this queue. As we show, avoiding this unrealistic spillback while retaining FIFO in the node model requires complicated network topologies. Our second contribution is a “partial FIFO” mechanism that avoids this unrealistic spillback, and a (first-order) node model and solution algorithm that incorporates this mechanism. The partial FIFO mechanism is parameterized through intervals that describe how individual movements influence each other, can be intuitively described from physical lane geometry and turning movement rules, and allows tuning to describe a link as having anything between full FIFO and no FIFO. Excepting the FIFO constraint, the present node model also fits within the well-established “general class of first-order node models” for multi-commodity flows. Several illustrative examples are presented.

Includes members:
  Member "expert level control of ramp metering based on multi task deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using reinforcement learning with partial vehicle detection for intelligent traffic signal control")
  Member "generic second order macroscopic traffic node model for general multi input multi output road junctions via a dynamic system approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a dynamic system based approach to modeling driver movements across general purpose managed lane interfaces")
  Member "fusing loop and gps probe measurements to estimate freeway density":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for robust assimilation of potentially malign third party data and its statistical meaning")
  Member "a dynamic system characterization of road network node models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a dynamic system based approach to modeling driver movements across general purpose managed lane interfaces")

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)