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: "hierarchical solution of markov decision processes using macro actions"
  Abstract: We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-actions and leave the state space unchanged, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macroactions as local policies that act in certain regions of state space, and by restricting states in the abstract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss several ways in which macro-actions can be generated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational overhead of macro-action generation.

Connected through hyperedge HE-1 (induced by source paper "options of interest temporal abstraction with interest functions"):
  with members:
    - "the termination critic"
    - "feudal networks for hierarchical reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "adaptive skills adaptive partitions asap"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"

Connected through hyperedge HE-2 (induced by source paper "efficient bimanual manipulation using learned task schemas"):
  with members:
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "active model learning and diverse action sampling for task and motion planning"
    - "learning to guide task and motion planning using score space representation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "meta learning shared hierarchies"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "advantage amplification in slowly evolving latent state environments"):
  with members:
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with a corrupted reward channel"
    - "taming the noise in reinforcement learning via soft updates"
    - "trust region policy optimization"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-4 (induced by source paper "hybrid planning for dynamic multimodal stochastic shortest paths"):
  with members:
    - "dynamic real time multimodal routing with hierarchical hybrid planning"
    - "an anytime algorithm for task and motion mdps"
    - "benchmarking deep reinforcement learning for continuous control"
    - "solving factored mdps with hybrid state and action variables"
    - "the ff planning system fast plan generation through heuristic search"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "an introduction to deep reinforcement learning"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "learning to play with intrinsically motivated self aware agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "a study on overfitting in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning robust options"):
  with members:
    - "deep robust kalman filter"
    - "situationally aware options"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "the option critic architecture"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-7 (induced by source paper "explainable deterministic mdps"):
  with members:
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-8 (induced by source paper "options discovery with budgeted reinforcement learning"):
  with members:
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "learning and transfer of modulated locomotor controllers"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "variational intrinsic control"
    - "strategic attentive writer for learning macro actions"
    - "hierarchical multiscale recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)