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 "On the Computational Economics of Reinforcement Learning"):
  Abstract: Following terminology used in adaptive control, we distinguish between indirect learning methods, which learn explicit models of the dynamic structure of the system to be controlled, and direct learning methods, which do not. We compare an existing indirect method, which uses a conventional dynamic programming algorithm, with a closely related direct reinforcement learning method by applying both methods to an infinite horizon Markov decision problem with unknown state-transition probabilities. The simulations show that although the direct method requires much less space and dramatically less computation per control action, its learning ability in this task is superior to, or compares favorably with, that of the more complex indirect method. Although these results do not address how the methods' performances compare as problems become more difficult, they suggest that given a fixed amount of computational power available per control action, it may be better to use a direct reinforcement learning method augmented with indirect techniques than to devote all available resources to a computation-ally costly indirect method. Comprehensive answers to the questions raised by this study depend on many factors making up the eco nomic context of the computation.

Includes members:
  Member "Learning to Act using Real-Time Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Complexity of Solving Markov Decision Problems")
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Strategy Learning with Multilayer Connectionist Representations 1":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Reinforcement Learning with Modular Neural Networks for Control")

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