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: "model based control using koopman operators"
  Abstract: This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting model-based control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-driven model for an unknown dynamical process that is useful for model-based control synthesis. Simulated results show that with increasing complexity in the choice of the basis functions, a closed-loop controller is able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore, the specification of the basis function are shown to be of importance when generating a Koopman operator for specific robotic systems. Experimental results with the Sphero SPRK robot explore the utility of the Koopman operator in a reduced state representation setting where increased complexity in the basis function improve open- and closed-loop controller performance in various terrains, including sand.

Connected through hyperedge HE-1 (induced by source paper "koopman based control of a soft continuum manipulator under variable loading conditions"):
  with members:
    - "comparison and experimental validation of predictive models for soft fiber reinforced actuators"
    - "active learning of dynamics for data driven control using koopman operators"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-2 (induced by source paper "learning compositional koopman operators for model based control"):
  with members:
    - "deep variational koopman models inferring koopman observations for uncertainty aware dynamics modeling and control"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "graph networks as learnable physics engines for inference and control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

Connected through hyperedge HE-3 (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"
    - "pulse based control using koopman operator under parametric uncertainty"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (induced by source paper "highly parallelized data driven mpc for minimal intervention shared control"):
  with members:
    - "operation and imitation under safety aware shared control"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "on infusing reachability based safety assurance within probabilistic planning frameworks for human robot vehicle interactions"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"

Connected through hyperedge HE-6 (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"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"

Connected through hyperedge HE-7 (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"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"

Connected through hyperedge HE-8 (induced by source paper "structured neural network dynamics for model based control"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"
    - "embed to control a locally linear latent dynamics model for control from raw images"

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)