human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Expected Mistake Bound Model for On-Line Reinforcement Learning"
  Abstract: We propose a model of efficient on-line reinforcement learning based on the expected mistake bound framework introduced by Haussler, Littlestone and Warmuth (1987). The measure of performance we use is the expected difference between the total reward received by the learning agent and that received by an agent behaving optimally from the start. We call this expected difference the cumulative mistake of the agent and we require that it "levels off" at a reasonably fast rate as the learning progresses. We show that this model is polynomially equivalent to the PAC model of off-line reinforcement learning introduced in (Fiechter, 1994). In particular we show how an off-line PAC reinforcement learning algorithm can be transformed into an efficient on-line algorithm in a simple and practical way. An immediate consequence of this result is that the PAC algorithm for the general finite state-space reinforcement learning problem described in (Fiechter, 1994) can be transformed into a polynomial on-line al gorithm with guaranteed performances.

Connected through hyperedge HE-1 (induced by source paper "Associative Reinforcement Learning: Functions in k-DNF"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Associative Reinforcement Learning: A Generate and Test Algorithm"
    - "Exploratory Learning in the Game of GO"

Connected through hyperedge HE-2 (induced by source paper "PAC Adaptive Control of Linear Systems"):
  with members:
    (none)

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Theory