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 linearly solvable markov decision problems"
  Abstract: We present a hierarchical reinforcement learning framework that formulates each task in the hierarchy as a special type of Markov decision process for which the Bellman equation is linear and has analytical solution. Problems of this type, called linearly-solvable MDPs (LMDPs) have interesting properties that can be exploited in a hierarchical setting, such as efficient learning of the optimal value function or task compositionality. The proposed hierarchical approach can also be seen as a novel alternative to solving LMDPs with large state spaces. We derive a hierarchical version of the so-called Z-learning algorithm that learns different tasks simultaneously and show empirically that it significantly outperforms the state-of-the-art learning methods in two classical hierarchical reinforcement learning domains: the taxi domain and an autonomous guided vehicle task.

Connected through hyperedge HE-1 (induced by source paper "constraint satisfaction propagation non stationary policy synthesis for temporal logic planning"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "temporal logic control of general markov decision processes by approximate policy refinement"
    - "rudder return decomposition for delayed rewards"
    - "variational option discovery algorithms"
    - "eigenoption discovery through the deep successor representation"
    - "the eigenoption critic framework"
    - "hierarchical subtask discovery with non negative matrix factorization"
    - "environment independent task specifications via gltl"

Connected through hyperedge HE-2 (induced by source paper "multi level discovery of deep options"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "learning from the memory of atari 2600"
    - "learning and transfer of modulated locomotor controllers"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "ddco discovery of deep continuous options for robot learning from demonstrations"):
  with members:
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "mutual alignment transfer learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "robust imitation of diverse behaviors"
    - "multi level discovery of deep options"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-4 (induced by source paper "hierarchy through composition with linearly solvable markov decision processes"):
  with members:
    - "successor features for transfer in reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)