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: "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
  Abstract: Macquarie University Technical Report No. 95-173C Department of Computing School of MPCE, Macquarie University, New South Wales, Australia

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"
    - "In  Active Learning with Statistical Models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "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"
    - "Exploration Bonuses and Dual Control"

Connected through hyperedge HE-3 (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"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-4 (induced by source paper "Query by Committee"):
  with members:
    - "Sifting informative examples from a random source."
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Active Learning with Committees for Text Categorization"
    - "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
    - "On-Site 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: Theory