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: "modeling and control of soft robots using the koopman operator and model predictive control"
  Abstract: Controlling soft robots with precision is a challenge due in large part to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman Operator Theory offers a way to construct explicit linear dynamical models of soft robots and to control them using established model-based linear control methods. This method is data-driven, yet unlike other data-driven models such as neural networks, it yields an explicit control-oriented linear model rather than just a "black-box" input-output mapping. This work describes this Koopman-based system identification method and its application to model predictive controller design. A model and MPC controller of a pneumatic soft robot arm was constructed via the method, and its performance was evaluated over several trajectory following tasks in the real-world. On all of the tasks, the Koopman-based MPC controller outperformed a benchmark MPC controller based on a linear state-space model of the same system.

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"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "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"
    - "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"
    - "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"
    - "flexible neural representation for physics prediction"

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)