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: "In  Active Learning with Statistical Models"
  Abstract: For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regres sion are both efficient and accurate.

Connected through hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Improving Generalization with Active Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Forward models: Supervised learning with a distal teacher"
    - "Efficient Locally Weighted Polynomial Regression Predictions"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Locally Weighted Learning for Control"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-3 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Exploration Bonuses and Dual Control"

Connected through hyperedge HE-4 (induced by source paper "Supervised learning from incomplete data via an EM approach"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Recurrent Neural Networks for Missing or Asynchronous Data"
    - "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"
    - "EM Algorithms for PCA and SPCA"
    - "A Statistical Approach to Decision Tree Modeling"
    - "Learning Bayesian Networks from Incomplete Data"

Connected through hyperedge HE-5 (induced by source paper "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."):
  with members:
    - "Information-based objective functions for active data selection"
    - "Query by Committee"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-6 (induced by source paper "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"):
  with members:
    - "Information-based objective functions for active data selection"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-7 (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"
    - "Information-based objective functions for active data selection"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-8 (induced by source paper "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"):
  with members:
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

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