human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"):
  Abstract: We present a control method for improved repetitive path following for a ground vehicle that is geared towards long-term operation where the operating conditions can change over time and are initially unknown. We use weighted Bayesian Linear Regression (wBLR) to model the unknown dynamics, and show how this simple model is more accurate in both its estimate of the mean behaviour and model uncertainty than Gaussian Process Regression and generalizes to novel operating conditions with little or no tuning. In addition, wBLR allows us to use fast adaptation and long-term learning in one, unified framework, to adapt quickly to new operating conditions and learn repetitive model errors over time. This comes with the added benefit of lower computational cost, longer look-ahead, and easier optimization when the model is used in a stochastic Model Predictive Controller (MPC). In order to fully capitalize on the long prediction horizons that are possible with this new approach, we use Tube MPC to reduce the growth of predicted uncertainty. We demonstrate the effectiveness of our approach in experiment on a 900\,kg ground robot showing results over 3.0\,km of driving with both physical and artificial changes to the robot's dynamics. All of our experiments are conducted using a stereo camera for localization.

Includes members:
  Member "cautious model predictive control using gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot")
  Member "bayesian optimization with automatic prior selection for data efficient direct policy search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors")
  Member "deep neural networks for improved impromptu trajectory tracking of quadrotors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "catch the ball accurate high speed motions for mobile manipulators via inverse dynamics learning")
  Member "one shot learning of manipulation skills with online dynamics adaptation and neural network priors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper meta reinforcement learning with sparse reward")
  Member "model based reinforcement learning with parametrized physical models and optimism driven exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization")

Question: Please tell me which class the source paper 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)