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: "strategic attentive writer for learning macro actions"
  Abstract: We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environment. It can also partition this internal representation into contiguous sub- sequences by learning for how long the plan can be committed to - i.e. followed without re-planing. Combining these properties, the proposed model, dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally abstracted macro- actions of varying lengths that are solely learnt from data without any prior information. These macro-actions enable both structured exploration and economic computation. We experimentally demonstrate that STRAW delivers strong improvements on several ATARI games by employing temporally extended planning strategies (e.g. Ms. Pacman and Frostbite). It is at the same time a general algorithm that can be applied on any sequence data. To that end, we also show that when trained on text prediction task, STRAW naturally predicts frequent n-grams (instead of macro-actions), demonstrating the generality of the approach.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "hierarchical policy learning is sensitive to goal space design"
    - "interpretable neural predictions with differentiable binary variables"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"

Connected through hyperedge HE-3 (induced by source paper "autonomous industrial management via reinforcement learning self learning agents for decision making a review"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "a web page classifier library based on random image content analysis using deep learning"
    - "imitation learning with concurrent actions in 3d games"
    - "one shot visual imitation learning via meta learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "deep reinforcement learning a brief survey"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "construction of macro actions for deep reinforcement learning"):
  with members:
    - "macro action reinforcement learning with sequence disentanglement using variational autoencoder"
    - "meta learning shared hierarchies"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning and transfer of modulated locomotor controllers"

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"
    - "data efficient hierarchical reinforcement learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning with structured hierarchical grammar representations of actions"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "macro action reinforcement learning with sequence disentanglement using variational autoencoder"):
  with members:
    - "time series segmentation through automatic feature learning"
    - "learning to walk via deep reinforcement learning"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning with macro actions"
    - "deep reinforcement learning in parameterized action space"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "modular deep reinforcement learning with temporal logic specifications"):
  with members:
    - "logically constrained neural fitted q iteration"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "modular multitask reinforcement learning with policy sketches"
    - "quantitative approximation of the probability distribution of a markov process by formal abstractions"
    - "continuous control with deep reinforcement learning"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

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)