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: "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
  Abstract: This paper deals with a new accelerated path integral method, which iteratively searches optimal controls with a small number of iterations. This study is based on the recent observations that a path integral method for reinforcement learning can be interpreted as gradient descent. This observation also applies to an iterative path integral method for optimal control, which sets a convincing argument for utilizing various optimization methods for gradient descent, such as momentum-based acceleration, step-size adaptation and their combination. We introduce these types of methods to the path integral and demonstrate that momentum-based methods, like Nesterov Accelerated Gradient and Adam, can significantly improve the convergence rate to search for optimal controls in simulated control systems. We also demonstrate that the accelerated path integral could improve the performance on model predictive control for various vehicle navigation tasks. Finally, we represent this accelerated path integral method as a recurrent network, which is the accelerated version of the previously proposed path integral networks (PI-Net). We can train the accelerated PI-Net more efficiently for inverse optimal control with less RAM than the original PI-Net.

Connected through hyperedge HE-1 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference"):
  with members:
    - "domain adversarial and conditional state space model for imitation learning"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "variational recurrent models for solving partially observable control tasks"
    - "deep dynamics models for learning dexterous manipulation"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "bayesian variational autoencoders for unsupervised out of distribution detection"

Connected through hyperedge HE-2 (induced by source paper "variational inference mpc for bayesian model based reinforcement learning"):
  with members:
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "an online learning approach to model predictive control"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "mirror descent search and its acceleration"
    - "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-3 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "mirror descent search and its acceleration"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "adaptive online learning in dynamic environments"
    - "risk sensitive path integral control"
    - "theoretical foundation for cma es from information geometric perspective"

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)