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: "data efficient hierarchical reinforcement learning"
  Abstract: Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higher and lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higher- and lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "multitask soft option learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to explore with meta policy gradient"
    - "evolved policy gradients"
    - "variational option discovery algorithms"

Connected through hyperedge HE-3 (induced by source paper "keeping your distance solving sparse reward tasks using self balancing shaped rewards"):
  with members:
    - "competitive experience replay"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"
    - "region growing curriculum generation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "coride joint order dispatching and fleet management for multi scale ride hailing platforms"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "colight learning network level cooperation for traffic signal control"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "policy search in continuous action domains an overview"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-5 (induced by source paper "composing ensembles of policies with deep reinforcement learning"):
  with members:
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to compose skills"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-6 (induced by source paper "disentangled skill embeddings for reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "multi task deep reinforcement learning with popart"
    - "meta reinforcement learning of structured exploration strategies"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "maximum a posteriori policy optimisation"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "hierarchical critics assignment for multi agent reinforcement learning"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "unity a general platform for intelligent agents"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep hierarchical reinforcement learning based recommendations via multi goals abstraction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "deep reinforcement learning for list wise recommendations"
    - "feudal networks for hierarchical reinforcement learning"
    - "wide deep learning for recommender systems"
    - "session based recommendations with recurrent neural networks"
    - "deep reinforcement learning in large discrete action spaces"

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)