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: "bayesian policy reuse"
  Abstract: A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires 'fast' responses, in terms of rapid convergence, especially when the task instance has a short duration, such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library, rather than policy learning from scratch. In policy reuse, the agent has prior knowledge of the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse, and present an algorithm for efficiently responding to a novel task instance by reusing a policy from the library of existing policies, where the choice is based on observed 'signals' which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.

Connected through hyperedge HE-1 (induced by source paper "contextual policy reuse using deep mixture models"):
  with members:
    - "model based reinforcement learning for atari"
    - "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"
    - "context aware policy reuse"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-2 (induced by source paper "context aware policy reuse"):
  with members:
    - "target transfer q learning and its convergence analysis"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "meta learning shared hierarchies"
    - "learning with options that terminate off policy"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "a multi armed bandit approach for online expert selection in markov decision processes"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-3 (induced by source paper "an optimal online method of selecting source policies for reinforcement learning"):
  with members:
    - "progressive neural networks"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "sample complexity of multi task reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "optimal spline trajectories by modelling kinematic constraints in robot soccer"):
  with members:
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"

Connected through hyperedge HE-5 (induced by source paper "bayesian optimisation with prior reuse for motion planning in robot soccer"):
  with members:
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"

Connected through hyperedge HE-6 (induced by source paper "hidden parameter markov decision processes a semiparametric regression approach for discovering latent task parametrizations"):
  with members:
    - "sample complexity of multi task reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "learning parameterized skills"

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)