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 "Neural Network Exploration Using Optimal Experiment Design"):
  Abstract: We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. The author was also funded by ATR Human Information Processing Laboratories, Siemens Corporate Research and NSF grant CDA-9309300.

Includes members:
  Member "In:  A Mixture Model System for Medical and Machine Diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection")
  Member "In  Active Learning with Statistical Models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Information-based objective functions for active data selection")
  Member "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Achieve Goals")
  Member "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Information-based objective functions for active data selection")
  Member "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Information-based objective functions for active data selection")
  Member "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks.":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Information-based objective functions for active data selection")
  Member "Exploration Bonuses and Dual Control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Q-Learning for Bandit Problems")

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: Neural_Networks