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 "An Introspection Approach to Querying a Trainer"):
  Abstract: Technical Report 96-13 January 22, 1996 Abstract This paper introduces the Introspection Approach, a method by which a learning agent employing reinforcement learning can decide when to ask a training agent for instruction. When using our approach, we find that the same number of trainer's responses produced significantly faster learners than by having the learner ask for aid randomly. Guidance received via our approach is more informative than random guidance. Thus, we can reduce the interaction that the training agent has with the learning agent without reducing the speed with which the learner develops its policy. In fact, by being intelligent about when the learner asks for help, we can even increase the learning speed for the same level of trainer interaction.

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 "Learning from an Automated Training Agent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")

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