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: "gpu based path integral control with learned dynamics"
  Abstract: We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resulting algorithm runs in a receding-horizon fashion in realtime, and is subject to no restrictive assumptions about costs, constraints, or dynamics. A simple change to the path integral control formulation allows the algorithm to take model uncertainty into account during planning, and we demonstrate its performance on a quadrotor navigation task. In addition to this novel adaptation of path integral control, this is the first time that a receding-horizon implementation of iterative path integral control has been run on a real system.

Connected through hyperedge HE-1 (induced by source paper "locally weighted regression pseudo rehearsal for online learning of vehicle dynamics"):
  with members:
    - "autorally an open platform for aggressive autonomous driving"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "learning to adapt meta learning for model based control"
    - "continual learning with deep generative replay"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "fearnet brain inspired model for incremental learning"
    - "deep visual foresight for planning robot motion"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-2 (induced by source paper "real time stochastic optimal control for multi agent quadrotor systems"):
  with members:
    - "latent kullback leibler control for continuous state systems using probabilistic graphical models"
    - "outdoor flocking and formation flight with autonomous aerial robots"
    - "probabilistic planning for continuous dynamic systems under bounded risk"
    - "path integral control by reproducing kernel hilbert space embedding"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-3 (induced by source paper "model predictive path integral control using covariance variable importance sampling"):
  with members:
    - "real time stochastic optimal control for multi agent quadrotor systems"

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)