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: "learning models for shared control of human machine systems with unknown dynamics"
  Abstract: We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-the-loop systems further in the discussion.

Connected through hyperedge HE-1 (induced by source paper "active learning of dynamics for data driven control using koopman operators"):
  with members:
    - "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"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "controlling assistive robots with learned latent actions"):
  with members:
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"
    - "imitating latent policies from observation"
    - "harmonic a multimodal dataset of assistive human robot collaboration"
    - "shared autonomy via deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "autonomy infused teleoperation with application to bci manipulation"

Connected through hyperedge HE-3 (induced by source paper "operation and imitation under safety aware shared control"):
  with members:
    - "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"
    - "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-4 (induced by source paper "highly parallelized data driven mpc for minimal intervention shared control"):
  with members:
    - "operation and imitation under safety aware shared control"
    - "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"
    - "model based control using koopman operators"

Connected through hyperedge HE-5 (induced by source paper "data driven gait segmentation for walking assistance in a lower limb assistive device"):
  with members:
    - "online user assessment for minimal intervention during task based robotic assistance"
    - "model based control using koopman operators"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"

Connected through hyperedge HE-6 (induced by source paper "where do you think you re going inferring beliefs about dynamics from behavior"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "shared autonomy via deep reinforcement learning"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "impossibility of deducing preferences and rationality from human policy"
    - "reinforcement learning with deep energy based policies"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-7 (induced by source paper "shared autonomy via deep reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "human robot mutual adaptation in shared autonomy"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "learning language games through interaction"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

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"
    - "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"
    - "model based control using koopman operators"
    - "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)