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 multi level hierarchies with hindsight"
  Abstract: Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously. Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies. In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies. The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal. We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods. Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.

Connected through hyperedge HE-1 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "value propagation networks"
    - "hindsight experience replay"

Connected through hyperedge HE-2 (induced by source paper "on simple reactive neural networks for behaviour based reinforcement learning"):
  with members:
    - "vid2param online system identification from video for robotics applications"
    - "composable deep reinforcement learning for robotic manipulation"
    - "silhonet an rgb method for 6d object pose estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "investigating human priors for playing video games"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "towards intelligent pick and place assembly of individualized products using reinforcement learning"):
  with members:
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "feudal networks for hierarchical reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "planning with goal conditioned policies"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "diagnosing and enhancing vae models"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "hrl4in hierarchical reinforcement learning for interactive navigation with mobile manipulators"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"

Connected through hyperedge HE-7 (induced by source paper "hierarchical reinforcement learning with advantage based auxiliary rewards"):
  with members:
    - "hierarchical policy learning is sensitive to goal space design"
    - "trust region policy optimization of pomdps"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "diversity is all you need learning skills without a reward function"
    - "context aware policy reuse"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "data efficient hierarchical reinforcement learning"
    - "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"
    - "meta learning shared hierarchies"

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.AI(Artificial Intelligence)