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: "mpc inspired neural network policies for sequential decision making"
  Abstract: In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces. We provide an extensive comparison of neural network policies by considering feed forward policies, recurrent policies, and recurrent policies with planning structure inspired by the Path Integral control framework. Our results suggest that MPC-type recurrent policies have better robustness to disturbances and modeling error.

Connected through hyperedge HE-1 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-3 (induced by source paper "pontryagin differentiable programming an end to end learning and control framework"):
  with members:
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "maximum principle based algorithms for deep learning"
    - "path integral networks end to end differentiable optimal control"
    - "deep learning approximation for stochastic control problems"
    - "value iteration networks"
    - "control of memory active perception and action in minecraft"

Connected through hyperedge HE-4 (induced by source paper "aren assured relu nn architecture for model predictive control of lti systems"):
  with members:
    - "fast efficient hyperparameter tuning for policy gradients"
    - "taking human out of learning applications a survey on automated machine learning"
    - "learning an approximate model predictive controller with guarantees"
    - "end to end learning for self driving cars"
    - "understanding deep neural networks with rectified linear units"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "neural network architectures for stochastic control using the nonlinear feynman kac lemma"):
  with members:
    - "deep dynamical modeling and control of unsteady fluid flows"
    - "differentiable mpc for end to end planning and control"
    - "deep learning approximation for stochastic control problems"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "universal planning networks"):
  with members:
    - "zero shot visual imitation"
    - "hindsight experience replay"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "value prediction network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-7 (induced by source paper "differentiable mpc for end to end planning and control"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "zero shot visual imitation"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-8 (induced by source paper "safe end to end imitation learning for model predictive control"):
  with members:
    - "deep predictive models for collision risk assessment in autonomous driving"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "adaptive probabilistic trajectory optimization via efficient approximate inference"
    - "very deep convolutional networks for large scale image recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "a reduction of imitation learning and structured prediction to no regret online 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.LG(Machine Learning)