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: "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
  Abstract: This paper presents a new model-based algorithm that computes predictive optimal controls online and in a closed loop for traditionally challenging nonlinear systems. Examples demonstrate the same algorithm controlling hybrid impulsive, underactuated, and constrained systems using only high-level models and trajectory goals. Rather than iteratively optimizing finite horizon control sequences to minimize an objective, this paper derives a closed-form expression for individual control actions, i.e., control values that can be applied for short duration, that optimally improve a tracking objective over a long time horizon. Under mild assumptions, actions become linear feedback laws near equilibria that permit stability analysis and performance-based parameter selection. Globally, optimal actions are guaranteed existence and uniqueness. By sequencing these actions online, in receding horizon fashion, the proposed controller provides a min–max constrained response to a state that avoids the overhead typically required to impose control constraints. Benchmark examples show that the approach can avoid local minima and outperform nonlinear optimal controllers and recent case-specific methods in terms of tracking performance and at speeds that are orders of magnitude faster than traditionally achievable ones.

Connected through hyperedge HE-1 (induced by source paper "active learning of dynamics for data driven control using koopman operators"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "learning deep neural network representations for koopman operators of nonlinear dynamical systems"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "dynamic task execution using active parameter identification with the baxter research robot"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "model based control using koopman operators"
    - "pulse based control using koopman operator under parametric uncertainty"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "operation and imitation under safety aware shared control"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "online user assessment for minimal intervention during task based robotic assistance"
    - "shared autonomy via deep reinforcement learning"
    - "dart noise injection for robust imitation learning"
    - "safe reinforcement learning via shielding"
    - "model based control using koopman operators"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-3 (induced by source paper "task based hybrid shared control for training through forceful interaction"):
  with members:
    - "iterative sequential action control for stable model based control of nonlinear systems"
    - "ergodic exploration of distributed information"
    - "algorithm for optimal mode scheduling in switched systems"

Connected through hyperedge HE-4 (induced by source paper "data driven gait segmentation for walking assistance in a lower limb assistive device"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "online user assessment for minimal intervention during task based robotic assistance"
    - "model based control using koopman operators"

Connected through hyperedge HE-5 (induced by source paper "modeling and control of soft robots using the koopman operator and model predictive control"):
  with members:
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "model based control using koopman operators"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"

Connected through hyperedge HE-6 (induced by source paper "decentralized ergodic control distribution driven sensing and exploration for multiagent systems"):
  with members:
    - "distributed active state estimation with user specified accuracy"
    - "real time area coverage and target localization using receding horizon ergodic exploration"
    - "ergodic exploration using binary sensing for nonparametric shape estimation"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-7 (induced by source paper "sampling based polytopic trees for approximate optimal control of piecewise affine systems"):
  with members:
    - "reactive planar manipulation with convex hybrid mpc"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "online user assessment for minimal intervention during task based robotic assistance"):
  with members:
    - "enabling robots to communicate their objectives"

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.RO(Robotics)