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: "Query by Committee"
  Abstract: We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.

Connected through hyperedge HE-1 (induced by source paper "Active Learning with Committees for Text Categorization"):
  with members:
    - "Improving Generalization with Active Learning"
    - "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
    - "Applying Winnow to Context-Sensitive Spelling Correction"

Connected through hyperedge HE-2 (induced by source paper "On-Site Learning"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"):
  with members:
    - "Active Learning with Committees for Text Categorization"

Connected through hyperedge HE-4 (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"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-5 (induced by source paper "Sifting informative examples from a random source."):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "A General Lower Bound on the Number of Examples Needed for Learning"

Connected through hyperedge HE-6 (induced by source paper "Heterogeneous Uncertainty Sampling for Supervised Learning"):
  with members:
    - "More Efficient Windowing"
    - "Information-based objective functions for active data selection"
    - "Learning Trees and Rules with Set-valued Features"
    - "Context-sensitive learning methods for text categorization"

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