human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Q-Learning for Bandit Problems"):
  Abstract: Multi-armed bandits may be viewed as decompositionally-structured Markov decision processes (MDP's) with potentially very-large state sets. A particularly elegant methodology for computing optimal policies was developed over twenty ago by Gittins [Gittins & Jones, 1974]. Gittins' approach reduces the problem of finding optimal policies for the original MDP to a sequence of low-dimensional stopping problems whose solutions determine the optimal policy through the so-called "Gittins indices." Katehakis and Veinott [Katehakis & Veinott, 1987] have shown that the Gittins index for a process in state i may be interpreted as a particular component of the maximum-value function associated with the "restart-in-i" process, a simple MDP to which standard solution methods for computing optimal policies, such as successive approximation, apply. This paper explores the problem of learning the Git-tins indices on-line without the aid of a process model; it suggests utilizing process-state-specific Q-learning agents to solve their respective restart-in-state-i subproblems, and includes an example in which the online reinforcement learning approach is applied to a problem of stochastic scheduling|one instance drawn from a wide class of problems that may be formulated as bandit problems.

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Towards a Reactive Critic")
  Member "Exploration Bonuses and Dual Control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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: Reinforcement_Learning