human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"):
  with members:
    - "Information-based objective functions for active data selection"

Connected through hyperedge HE-2 (induced by source paper "In  Active Learning with Statistical Models"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Information-based objective functions for active data selection"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-3 (induced by source paper "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Information-based objective functions for active data selection"
    - "Query by Committee"

Connected through hyperedge HE-4 (induced by source paper "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Information-based objective functions for active data selection"

Connected through hyperedge HE-5 (induced by source paper "In:  A Mixture Model System for Medical and Machine Diagnosis"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "Information-based objective functions for active data selection"

Connected through hyperedge HE-6 (induced by source paper "Exploration Bonuses and Dual Control"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Q-Learning for Bandit Problems"

Connected through hyperedge HE-7 (induced by source paper "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"):
  with members:
    - "Learning to Achieve Goals"

Question: Please tell me which class the center node 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