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: "on existence of separable contraction metrics for monotone nonlinear systems"
  Abstract: Finding separable certificates of stability is important for tractability of analysis methods for large-scale networked systems. In this paper we consider the question of when a nonlinear system which is contracting, i.e. all solutions are exponentially stable, can have that property verified by a separable metric. Making use of recent results in the theory of positive linear systems and separable Lyapunov functions, we prove several new results showing when this is possible, and discuss the application of to nonlinear distributed control design via convex optimization.

Connected through hyperedge HE-1 (induced by source paper "priority maps for surveillance and intervention of wildfires and other spreading processes"):
  with members:
    - "minimum energy control for complex networks"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "persistent robotic tasks monitoring and sweeping in changing environments"

Connected through hyperedge HE-2 (induced by source paper "control contraction metrics on finsler manifolds"):
  with members:
    - "contraction analysis of switched systems via regularization"
    - "nonlinear stabilization via control contraction metrics a pseudospectral approach for computing geodesics"
    - "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"
    - "a differential lyapunov framework for contraction analysis"
    - "logarithmic lipschitz norms and diffusion induced instability"

Connected through hyperedge HE-3 (induced by source paper "distributed nonlinear control design using separable control contraction metrics"):
  with members:
    - "separability of lyapunov functions for contractive monotone systems"
    - "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-4 (induced by source paper "a contractive approach to separable lyapunov functions for monotone systems"):
  with members:
    - "separability of lyapunov functions for contractive monotone systems"
    - "a compartmental model for traffic networks and its dynamical behavior"
    - "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)