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 "learning stabilizable dynamical systems via control contraction metrics"):
  Abstract: We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learned system can be accompanied by a robust controller capable of stabilizing any open-loop trajectory that the system may generate. By leveraging tools from contraction theory, statistical learning, and convex optimization, we provide a general and tractable semi-supervised algorithm to learn stabilizable dynamics, which can be applied to complex underactuated systems. We validated the proposed algorithm on a simulated planar quadrotor system and observed notably improved trajectory generation and tracking performance with the control-theoretic regularized model over models learned using traditional regression techniques, especially when using a small number of demonstration examples. The results presented illustrate the need to infuse standard model-based reinforcement learning algorithms with concepts drawn from nonlinear control theory for improved reliability.

Includes members:
  Member "learning contracting vector fields for stable imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teleoperator imitation with continuous time safety")
  Member "deep reinforcement learning in a handful of trials using probabilistic dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "random fourier features for operator valued kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning contracting vector fields for stable imitation learning")
  Member "operator valued bochner theorem fourier feature maps for operator valued kernels and vector valued learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning contracting vector fields for stable imitation learning")
  Member "control contraction metrics convex and intrinsic criteria for nonlinear feedback design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time funnel generation for restricted motion planning")

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)