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 "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"):
  Abstract: For an agent living in a non-deterministic Markov environment (NME), what is, in theory, the fastest way of acquiring information about its statistical properties? The answer is: to design "optimal" sequences of "experiments" by performing action sequences that maximize expected information gain. This notion is implemented by combining concepts from information theory and reinforcement learning. Experiments show that the resulting method, reinforcement driven information acquisition, can explore certain NMEs much faster than conventional random exploration.

Includes members:
  Member "In  Active Learning with Statistical Models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection")
  Member "Information-based objective functions for active data selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Neural Network Exploration Using Optimal Experiment Design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons")

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