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: "hierarchical reinforcement learning via advantage weighted information maximization"
  Abstract: Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task. In this paper, we propose an HRL method that learns a latent variable of a hierarchical policy using mutual information maximization. Our approach can be interpreted as a way to learn a discrete and latent representation of the state-action space. To learn option policies that correspond to modes of the advantage function, we introduce advantage-weighted importance sampling. In our HRL method, the gating policy learns to select option policies based on an option-value function, and these option policies are optimized based on the deterministic policy gradient method. This framework is derived by leveraging the analogy between a monolithic policy in standard RL and a hierarchical policy in HRL by using a deterministic option policy. Experimental results indicate that our HRL approach can learn a diversity of options and that it can enhance the performance of RL in continuous control tasks.

Connected through hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"
    - "kernelized movement primitives"

Connected through hyperedge HE-2 (induced by source paper "off policy maximum entropy reinforcement learning soft actor critic with advantage weighted mixture policy sac awmp"):
  with members:
    - "planning with expectation models"
    - "teach biped robots to walk via gait principles and reinforcement learning with adversarial critics"
    - "dac the double actor critic architecture for learning options"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"
    - "forward backward reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multipolar multi source policy aggregation for transfer reinforcement learning between diverse environmental dynamics"):
  with members:
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "meta learning a survey"
    - "residual policy learning"
    - "policy transfer with strategy optimization"
    - "an introduction to deep reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "residual reinforcement learning for robot control"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning with structured hierarchical grammar representations of actions"):
  with members:
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "model based reinforcement learning for atari"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "towards deep symbolic reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "inforl interpretable reinforcement learning using information maximization"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
    - "vime variational information maximizing exploration"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (induced by source paper "hierarchical reinforcement learning for quadruped locomotion"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "hierarchical reinforcement learning with hindsight"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "policy transfer with strategy optimization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "latent space policies for hierarchical 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.LG(Machine Learning)