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: "differentiable mpc for end to end planning and control"
  Abstract: We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we differentiate through MPC by using the KKT conditions of the convex approximation at a fixed point of the controller. Using this strategy, we are able to learn the cost and dynamics of a controller via end-to-end learning. Our experiments focus on imitation learning in the pendulum and cartpole domains, where we learn the cost and dynamics terms of an MPC policy class. We show that our MPC policies are significantly more data-efficient than a generic neural network and that our method is superior to traditional system identification in a setting where the expert is unrealizable.

Connected through hyperedge HE-1 (induced by source paper "objective mismatch in model based reinforcement learning"):
  with members:
    - "exploring model based planning with policy networks"
    - "deepmdp learning continuous latent space models for representation learning"
    - "when to trust your model model based policy optimization"
    - "robust reinforcement learning for continuous control with model misspecification"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "solar deep structured latent representations for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neural lyapunov model predictive control"):
  with members:
    - "deep value model predictive control"
    - "differentiable convex optimization layers"
    - "tustin neural networks a class of recurrent nets for adaptive mpc of mechanical systems"
    - "accelerating neural odes with spectral elements"
    - "when to trust your model model based policy optimization"
    - "pytorch an imperative style high performance deep learning library"
    - "lipschitz continuity in model based reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-3 (induced by source paper "q vae for disentangled representation learning and latent dynamical systems"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "towards a definition of disentangled representations"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "a closed form expression for the sharma mittal entropy of exponential families"

Connected through hyperedge HE-4 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-5 (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-6 (induced by source paper "search on the replay buffer bridging planning and reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "learning latent plans from play"
    - "the laplacian in rl learning representations with efficient approximations"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "learning navigation behaviors end to end with autorl"

Connected through hyperedge HE-7 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "dynamic filtering with large sampling field for convnets"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (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"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "on evaluation of embodied navigation agents"

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)