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: "autonomous extracting a hierarchical structure of tasks in reinforcement learning and multi task reinforcement learning"
  Abstract: Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL.

Connected through hyperedge HE-1 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies"):
  with members:
    - "neural logic machines"
    - "on first order meta learning algorithms"
    - "bayesian model agnostic meta learning"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "meta reinforcement learning of structured exploration strategies"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "probabilistic model agnostic meta learning"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-2 (induced by source paper "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"):
  with members:
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "neuro symbolic program synthesis"
    - "high dimensional continuous control using generalized advantage estimation"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "mazebase a sandbox for learning from games"

Connected through hyperedge HE-3 (induced by source paper "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"):
  with members:
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "unifying count based exploration and intrinsic motivation"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-4 (induced by source paper "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"):
  with members:
    - "optimal measurement policy for predicting uav network topology"
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"

Connected through hyperedge HE-5 (induced by source paper "meta learning shared hierarchies"):
  with members:
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "the option critic architecture"
    - "asynchronous methods for deep reinforcement learning"

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)