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: "control contraction metrics convex and intrinsic criteria for nonlinear feedback design"
  Abstract: We introduce the concept of a control contraction metric, extending contraction analysis to constructive nonlinear control design. We derive sufficient conditions for exponential stabilizability of all trajectories of a nonlinear control system. The conditions have a simple geometrical interpretation, can be written as a convex feasibility problem, and are invariant under coordinate changes. We show that these conditions are necessary and sufficient for feedback linearizable systems, and also derive novel convex criteria for exponential stabilization of a nonlinear submanifold of state space. We illustrate the benefits of convexity by constructing a controller for an unstable polynomial system that combines local optimality and global stability, using a metric found via sum-of-squares programming.

Connected through hyperedge HE-1 (induced by source paper "real time funnel generation for restricted motion planning"):
  with members:
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-2 (induced by source paper "robust contraction analysis of nonlinear systems via differential iqc"):
  with members:
    - "contraction analysis of switched systems via regularization"
    - "on differentially dissipative dynamical systems"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-3 (induced by source paper "a comparison of lpv gain scheduling and control contraction metrics for nonlinear control"):
  with members:
    - "distributed nonlinear control design using separable control contraction metrics"
    - "nonlinear stabilization via control contraction metrics a pseudospectral approach for computing geodesics"

Connected through hyperedge HE-4 (induced by source paper "learning stabilizable dynamical systems via control contraction metrics"):
  with members:
    - "learning contracting vector fields for stable imitation learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "random fourier features for operator valued kernels"
    - "operator valued bochner theorem fourier feature maps for operator valued kernels and vector valued learning"

Connected through hyperedge HE-5 (induced by source paper "control contraction metrics on finsler manifolds"):
  with members:
    - "on existence of separable contraction metrics for monotone nonlinear systems"
    - "contraction analysis of switched systems via regularization"
    - "nonlinear stabilization via control contraction metrics a pseudospectral approach for computing geodesics"
    - "a differential lyapunov framework for contraction analysis"
    - "logarithmic lipschitz norms and diffusion induced instability"

Connected through hyperedge HE-6 (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"
    - "separability of lyapunov functions for contractive monotone systems"
    - "a differential lyapunov framework for contraction analysis"

Connected through hyperedge HE-7 (induced by source paper "contracting nonlinear observers convex optimization and learning from data"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "convex parameterizations and fidelity bounds for nonlinear identification and reduced order modelling"
    - "a differential lyapunov framework for contraction analysis"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "on existence of separable contraction metrics for monotone nonlinear systems"):
  with members:
    - "separability of lyapunov functions for contractive monotone systems"
    - "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)