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 the construction of safe controllable regions for affine systems with applications to robotics"
  Abstract: This paper studies the problem of constructing in-block controllable (IBC) regions for affine systems. That is, we are concerned with constructing regions in the state space of affine systems such that all the states in the interior of the region are mutually accessible through the region's interior by applying uniformly bounded inputs. We first show that existing results for checking in-block controllability on given polytopic regions cannot be easily extended to address the question of constructing IBC regions. We then explore the geometry of the problem to provide a computationally efficient algorithm for constructing IBC regions. We also prove the soundness of the algorithm. We then use the proposed algorithm to construct safe speed profiles for different robotic systems, including fully-actuated robots, ground robots modeled as unicycles with acceleration limits, and unmanned aerial vehicles (UAVs). Finally, we present several experimental results on UAVs to verify the effectiveness of the proposed algorithm. For instance, we use the proposed algorithm for real-time collision avoidance for UAVs.

Connected through hyperedge HE-1 (induced by source paper "provably robust learning based approach for high accuracy tracking control of lagrangian systems"):
  with members:
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-2 (induced by source paper "multi robot transfer learning a dynamical system perspective"):
  with members:
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "learning contact rich manipulation skills with guided policy search"

Connected through hyperedge HE-3 (induced by source paper "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"):
  with members:
    - "safe controller optimization for quadrotors with gaussian processes"
    - "real time optimal control via deep neural networks study on landing problems"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "data efficient learning of feedback policies from image pixels using deep dynamical models"

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)