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: "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"
  Abstract: Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering. However, robotic skill learning methods typically make one of several trade-offs to enable practical real-world learning, such as requiring manually designed policy or value function representations, initialization from human-provided demonstrations, instrumentation of the training environment, or extremely long training times. In this paper, we propose a new reinforcement learning algorithm for learning manipulation skills that can train general-purpose neural network policies with minimal human engineering, while still allowing for fast, efficient learning in stochastic environments. Our approach builds on the guided policy search (GPS) algorithm, which transforms the reinforcement learning problem into supervised learning from a computational teacher (without human demonstrations). In contrast to prior GPS methods, which require a consistent set of initial states to which the system must be reset after each episode, our approach can handle randomized initial states, allowing it to be used in environments where deterministic resets are impossible. We compare our method to existing policy search techniques in simulation, showing that it can train high-dimensional neural network policies with the same sample efficiency as prior GPS methods, and present real-world results on a PR2 robotic manipulator.

Connected through hyperedge HE-1 (induced by source paper "dual policy iteration"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "on polynomial time pac reinforcement learning with rich observations"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "thinking fast and slow with deep learning and tree search"
    - "guided policy search as approximate mirror descent"
    - "learning to filter with predictive state inference machines"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "reset free trial and error learning for robot damage recovery"):
  with members:
    - "patchwork kriging for large scale gaussian process regression"
    - "quality and diversity optimization a unifying modular framework"
    - "limbo a fast and flexible library for bayesian optimization"
    - "robots that can adapt like animals"
    - "trust region policy optimization"
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"
    - "illuminating search spaces by mapping elites"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-3 (induced by source paper "transferring end to end visuomotor control from simulation to real world for a multi stage task"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "third person imitation learning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "feature control as intrinsic motivation for hierarchical reinforcement learning"
    - "one shot imitation learning"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-4 (induced by source paper "combining model based and model free updates for trajectory centric reinforcement learning"):
  with members:
    - "guided policy search as approximate mirror descent"
    - "continuous deep q learning with model based acceleration"
    - "path integral guided policy search"
    - "model free trajectory optimization for reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

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)