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 "control contraction metrics on finsler manifolds"):
  Abstract: Control Contraction Metrics (CCMs) provide a nonlinear controller design involving an offline search for a Riemannian metric and an online search for a shortest path between the current and desired trajectories. In this paper, we generalize CCMs to Finsler geometry, allowing the use of non-Riemannian metrics. We provide open loop and sampled data controllers. The sampled data control construction presented here does not require real time computation of globally shortest paths, simplifying computation.

Includes members:
  Member "on existence of separable contraction metrics for monotone nonlinear systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "priority maps for surveillance and intervention of wildfires and other spreading processes")
  Member "contraction analysis of switched systems via regularization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust contraction analysis of nonlinear systems via differential iqc")
  Member "nonlinear stabilization via control contraction metrics a pseudospectral approach for computing geodesics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comparison of lpv gain scheduling and control contraction metrics for nonlinear control")
  Member "control contraction metrics convex and intrinsic criteria for nonlinear feedback design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time funnel generation for restricted motion planning")
  Member "a differential lyapunov framework for contraction analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contracting implicit recurrent neural networks stable models with improved trainability")
  Member "logarithmic lipschitz norms and diffusion induced instability":
    also connected through other hyperedges:
      (none)

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)