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: "learning from the hindsight plan episodic mpc improvement"
  Abstract: Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the iterative learning setting, where the same task can be repeated several times, and propose a policy improvement scheme for MPC. The main idea is that between executions we can, offline, run MPC with a longer horizon, resulting in a hindsight plan. To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan. This effectively consolidates long-term reasoning into the short-horizon planning. We empirically evaluate our approach in contact-rich manipulation tasks both in simulated and real environments, such as peg insertion by a real PR2 robot.

Connected through hyperedge HE-1 (induced by source paper "information theoretic model predictive q learning"):
  with members:
    - "an online learning approach to model predictive control"
    - "deepmind control suite"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "model ensemble trust region policy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "model based active exploration"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "residual policy learning"
    - "learning robotic assembly from cad"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-3 (induced by source paper "leveraging experience in lazy search"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "fast policy learning through imitation and reinforcement"
    - "the provable virtue of laziness in motion planning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "learning heuristic search via imitation"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning on variable impedance controller for high precision robotic assembly"):
  with members:
    - "residual reinforcement learning for robot control"
    - "deep reinforcement learning for high precision assembly tasks"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "guided policy search as approximate mirror descent"
    - "asynchronous methods for deep reinforcement learning"
    - "path integral guided policy search"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "the openuav swarm simulation testbed a collaborative designstudio for field robotics"):
  with members:
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "geomorphological analysis using unpiloted aircraft systems structure from motion and deep learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-6 (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"
    - "mpc inspired neural network policies for sequential decision making"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning to predict ego vehicle poses for sampling based nonholonomic motion planning"):
  with members:
    - "learning human aware path planning with fully convolutional networks"
    - "universal planning networks"
    - "motion planning networks"
    - "learning sampling distributions for robot motion planning"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "data driven planning via imitation learning"
    - "end to end learning for self driving cars"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"):
  with members:
    - "data driven planning via imitation learning"
    - "pomdp lite for robust robot planning under uncertainty"
    - "configuration lattices for planar contact manipulation under uncertainty"
    - "perception aware path planning"
    - "learning to gather information via imitation"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "shared autonomy via hindsight optimization"
    - "on the complexity of solving markov decision problems"

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)