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: "separability of lyapunov functions for contractive monotone systems"
  Abstract: We consider constructing Lyapunov functions for systems that are both monotone and contractive with respect to a weighted one norm or infinity norm. This class of systems admits separable Lyapunov functions that are either the sum or the maximum of a collection of functions of a single argument. In either case, two classes of separable Lyapunov functions exist: the first class is separable along the system's state, and the second class is separable along components of the system's vector field. The latter case is advantageous for many practically motivated systems for which it is difficult to measure the system's state but easier to measure the system's velocity or rate of change. We provide several examples to demonstrate our results.

Connected through hyperedge HE-1 (induced by source paper "distributed nonlinear control design using separable control contraction metrics"):
  with members:
    - "on existence of separable contraction metrics for monotone nonlinear systems"
    - "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-2 (induced by source paper "on resilient control of dynamical flow networks"):
  with members:
    - "mixed monotonicity of partial first in first out traffic flow models"
    - "robust network routing under cascading failures"
    - "a compartmental model for traffic networks and its dynamical behavior"
    - "design of multi path data routing algorithm based on network reliability"

Connected through hyperedge HE-3 (induced by source paper "a contractive approach to separable lyapunov functions for monotone systems"):
  with members:
    - "on existence of separable contraction metrics for monotone nonlinear systems"
    - "a compartmental model for traffic networks and its dynamical behavior"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-4 (induced by source paper "on existence of separable contraction metrics for monotone nonlinear systems"):
  with members:
    - "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-5 (induced by source paper "approximating the frequency response of contractive systems"):
  with members:
    - "a differential lyapunov framework for contraction analysis"

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)