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: "data efficient reinforcement learning with probabilistic model predictive control"
  Abstract: Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms require a large number of interactions with the environment. A large number of interactions may be impractical in many real-world applications, such as robotics, and many practical systems have to obey limitations in the form of state space or control constraints. To reduce the number of system interactions while simultaneously handling constraints, we propose a model-based RL framework based on probabilistic Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainty into long-term predictions, thereby, reducing the impact of model errors. We then use MPC to find a control sequence that minimises the expected long-term cost. We provide theoretical guarantees for first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. We demonstrate that our approach does not only achieve state-of-the-art data efficiency, but also is a principled way for RL in constrained environments.

Connected through hyperedge HE-1 (induced by source paper "dynamics aware unsupervised discovery of skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "therml the thermodynamics of machine learning"
    - "model ensemble trust region policy optimization"
    - "soft actor critic algorithms and applications"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "green ai"):
  with members:
    - "sparse networks from scratch faster training without losing performance"
    - "defending against neural fake news"
    - "show your work improved reporting of experimental results"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "benchmarking model based reinforcement learning"):
  with members:
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-4 (induced by source paper "model imitation for model based reinforcement learning"):
  with members:
    - "combating the compounding error problem with a multi step model"
    - "benchmarking model based reinforcement learning"
    - "when to trust your model model based policy optimization"
    - "minegan effective knowledge transfer from gans to target domains with few images"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "spectral normalization for generative adversarial networks"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-5 (induced by source paper "model based lookahead reinforcement learning"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"
    - "value prediction network"

Connected through hyperedge HE-6 (induced by source paper "learning to guide guidance law based on deep meta learning and model predictive path integral control"):
  with members:
    - "adaptive guidance with reinforcement meta learning"
    - "deep haptic model predictive control for robot assisted dressing"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "learning image conditioned dynamics models for control of under actuated legged millirobots"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "control of a quadrotor with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "measuring the data efficiency of deep learning methods"):
  with members:
    - "spectral inference networks unifying spectral methods with deep learning"
    - "low shot learning from imaginary data"
    - "gradient based training of slow feature analysis by differentiable approximate whitening"
    - "learning feed forward one shot learners"
    - "improved graph based sfa information preservation complements the slowness principle"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "uncertainty aware model based policy optimization"):
  with members:
    - "model ensemble trust region policy optimization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"
    - "continuous control with deep reinforcement learning"

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)