human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  Abstract: Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

Includes members:
  Member "strategic object oriented reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization")
  Member "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "go explore a new approach for hard exploration problems")
  Member "robot representing and reasoning with knowledge from reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty")
  Member "programmatically interpretable reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning")
  Member "hierarchical imitation and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "explaining explanations an overview of interpretability of machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "eigenoption discovery through the deep successor representation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "option discovery in the absence of rewards with manifold analysis")

Question: Please tell me which class the source paper 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)