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: "exploration exploitation in mdps with options"
  Abstract: While a large body of empirical results show that temporally-extended actions and options may significantly affect the learning performance of an agent, the theoretical understanding of how and when options can be beneficial in online reinforcement learning is relatively limited. In this paper, we derive an upper and lower bound on the regret of a variant of UCRL using options. While we first analyze the algorithm in the general case of semi-Markov decision processes (SMDPs), we show how these results can be translated to the specific case of MDPs with options and we illustrate simple scenarios in which the regret of learning with options can be \textit{provably} much smaller than the regret suffered when learning with primitive actions.

Connected through hyperedge HE-1 (induced by source paper "hierarchical imitation and reinforcement learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "generating long term trajectories using deep hierarchical networks"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "approximate exploration through state abstraction"):
  with members:
    - "exploration by random network distillation"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "parameter space noise for exploration"
    - "near optimal behavior via approximate state abstraction"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "extreme state aggregation beyond mdps"

Connected through hyperedge HE-3 (induced by source paper "learning high level representations from demonstrations"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "modular multitask reinforcement learning with policy sketches"

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)