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: "clustering markov decision processes for continual transfer"
  Abstract: We consider the problem of a lifelong learning agent that continually encounters a sequence of tasks. After sufficient exposure to the domain, it is conceivable that the agent has developed a large library of task-specific optimal policies that may then be used as a basis for transfer learning - to speed up operation in a novel instance of a task from this domain. We present an algorithm for continual transfer learning in this setting of reinforcement learning with MDPs. A key question now is - is it possible to concisely encode the library to enable efficient transfer and search? Towards this end, we present a theory, framework and algorithms to cluster MDPs to select as source tasks a small subset of a large number of previous tasks. Our contributions are as follows. We present a principled policy-reuse algorithm that optimally reuses a given set of source policies when solving a particular MDP. We then present a framework for clustering the previously solved MDPs to obtain a source MDP set which optimize the reuse performance of our transfer algorithm. The framework consists of (i) a class of distance functions over MDPs which helps define clustering of MDPs; (ii) a cost function which measures how good a particular clustering is for generating useful source tasks for the transfer algorithm and (iii) provably convergent optimization algorithm for finding the optimal cluster. Finally, we present a set of experiments that illustrate the efficacy of our approach.

Connected through hyperedge HE-1 (induced by source paper "temple learning template of transitions for sample efficient multi task rl"):
  with members:
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "sample complexity of multi task reinforcement learning"
    - "incremental model based learners with formal learning time guarantees"

Connected through hyperedge HE-2 (induced by source paper "bayesian policy reuse"):
  with members:
    - "sample complexity of multi task reinforcement learning"
    - "model based bayesian exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "learning parameterized skills"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "contextual bandits with similarity information"

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)