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 resilient control of dynamical flow networks"):
  Abstract: Abstract   Resilience has become a key aspect in the design of contemporary infrastructure networks. This comes as a result of ever-increasing loads, limited physical capacity, and fast-growing levels of interconnectedness and complexity due to the recent technological advancements. The problem has motivated a considerable amount of research within the last few years, particularly focused on the dynamical aspects of network flows, complementing more classical static network flow optimization approaches.  In this tutorial paper, a class of single-commodity first-order models of dynamical flow networks is considered. A few results recently appeared in the literature and dealing with stability and robustness of dynamical flow networks are gathered and originally presented in a unified framework. In particular, (differential) stability properties of monotone dynamical flow networks are treated in some detail, and the notion of margin of resilience is introduced as a quantitative measure of their robustness. While emphasizing methodological aspects —including structural properties, such as monotonicity, that enable tractability and scalability— over the specific applications, connections to well-established road traffic flow models are made.

Includes members:
  Member "separability of lyapunov functions for contractive monotone systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed nonlinear control design using separable control contraction metrics")
  Member "mixed monotonicity of partial first in first out traffic flow models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tira toolbox for interval reachability analysis")
  Member "robust network routing under cascading failures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing optimal control of cascading failure in dc networks")
  Member "a compartmental model for traffic networks and its dynamical behavior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social cost guarantees in smart route guidance")
  Member "design of multi path data routing algorithm based on network reliability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust network routing under cascading failures")

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)