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: "guided meta policy search"
  Abstract: Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples because they learn from scratch. Meta-RL aims to address this challenge by leveraging experience from previous tasks in order to more quickly solve new tasks. However, in practice, these algorithms generally also require large amounts of on-policy experience during the meta-training process, making them impractical for use in many problems. To this end, we propose to learn a reinforcement learning procedure through imitation of expert policies that solve previously-seen tasks. This involves a nested optimization, with RL in the inner loop and supervised imitation learning in the outer loop. Because the outer loop imitation learning can be done with off-policy data, we can achieve significant gains in meta-learning sample efficiency. In this paper, we show how this general idea can be used both for meta-reinforcement learning and for learning fast RL procedures from multi-task demonstration data. The former results in an approach that can leverage policies learned for previous tasks without significant amounts of on-policy data during meta-training, whereas the latter is particularly useful in cases where demonstrations are easy for a person to provide. Across a number of continuous control meta-RL problems, we demonstrate significant improvements in meta-RL sample efficiency in comparison to prior work as well as the ability to scale to domains with visual observations.

Connected through hyperedge HE-1 (induced by source paper "provably convergent policy gradient methods for model agnostic meta reinforcement learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "online meta learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "meta learning with implicit gradients"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-2 (induced by source paper "meta learning with warped gradient descent"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "meta learning with differentiable convex optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning representations for continual learning"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (induced by source paper "meta reinforcement learning as task inference"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "emergent coordination through competition"
    - "deepmind control suite"
    - "neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "neural predictive belief representations"
    - "learning dexterous in hand manipulation"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "meta learning via learned loss"):
  with members:
    - "reward shaping via meta learning"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "generalized inner loop meta learning"
    - "meta gradient reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "evolved policy gradients"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-5 (induced by source paper "comyco quality aware adaptive video streaming via imitation learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "variance reduction for reinforcement learning in input driven environments"
    - "tiyuntsong a self play reinforcement learning approach for abr video streaming"
    - "cross layer effects on training neural algorithms for video streaming"
    - "dart noise injection for robust imitation learning"
    - "pruning convolutional neural networks for resource efficient inference"

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)