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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "enabling robots to understand incomplete natural language instructions using commonsense reasoning"
    - "from recognition to cognition visual commonsense reasoning"
    - "spatial knowledge distillation to aid visual reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "integrating human provided information into belief state representation using dynamic factorization"

Connected through hyperedge HE-2 (induced by source paper "bridging commonsense reasoning and probabilistic planning via a probabilistic action language"):
  with members:
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "a probabilistic extension of action language bc"
    - "integrating human provided information into belief state representation using dynamic factorization"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "computing lpmln using asp and mln solvers"
    - "probabilistic reasoning with answer sets"

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)