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: "learning representations in model free hierarchical reinforcement learning"
  Abstract: Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction. Abstraction can be had by identifying a relatively small set of states that are likely to be useful as subgoals, in concert with the learning of corresponding skill policies to achieve those subgoals. Many approaches to subgoal discovery in HRL depend on the analysis of a model of the environment, but the need to learn such a model introduces its own problems of scale. Once subgoals are identified, skills may be learned through intrinsic motivation, introducing an internal reward signal marking subgoal attainment. In this paper, we present a novel model-free method for subgoal discovery using incremental unsupervised learning over a small memory of the most recent experiences (trajectories) of the agent. When combined with an intrinsic motivation learning mechanism, this method learns both subgoals and skills, based on experiences in the environment. Thus, we offer an original approach to HRL that does not require the acquisition of a model of the environment, suitable for large-scale applications. We demonstrate the efficiency of our method on two RL problems with sparse delayed feedback: a variant of the rooms environment and the first screen of the ATARI 2600 Montezuma's Revenge game.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for combinatorial optimization a survey"):
  with members:
    - "fast detection of maximum common subgraph via deep q learning"
    - "combinatorial optimization by graph pointer networks and hierarchical reinforcement learning"
    - "co training for policy learning"
    - "exploratory combinatorial optimization with reinforcement learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to perform local rewriting for combinatorial optimization"
    - "latent space policies for hierarchical reinforcement learning"
    - "learning permutations with sinkhorn policy gradient"

Connected through hyperedge HE-2 (induced by source paper "efficient exploration through intrinsic motivation learning for unsupervised subgoal discovery in model free hierarchical reinforcement learning"):
  with members:
    - "learning sparse representations in reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "planning with abstract learned models while learning transferable subtasks"):
  with members:
    - "an investigation of model free planning"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "compositional planning using optimal option models"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-4 (induced by source paper "quasi newton optimization methods for deep learning applications"):
  with members:
    - "trust region policy optimization"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "learning sparse representations in reinforcement learning"):
  with members:
    - "quasi newton optimization in deep q learning for playing atari games"
    - "the utility of sparse representations for control in reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "a survey of sparse representation algorithms and applications"

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)