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: "Active Learning with Committees for Text Categorization"
  Abstract: In many real-world domains like text categorization, supervised learning requires a large number of training examples. In this paper we describe an active learning method that uses a committee of learners to reduce the number of training examples required for learning. Our approach is similar to the Query by Committee framework, where disagreement among the committee members on the predicted label for the input part of the example is used to signal the need for knowing the actual value of the label. Our experiments in text categorization using a committee of Winnow-based learners demonstrate that this approach can reduce the number of labeled training examples required over that used by a single Winnow learner by 1-2 orders of magnitude. This paper is not under review or accepted for publication in another conference or journal. Acknowledgements: The availability of the Reuters-22173 corpus [Reuters] and of the | STAT Data Manipulation and Analysis Programs [Perlman] has greatly assisted in our research to date.

Connected through hyperedge HE-1 (induced by source paper "Applying Winnow to Context-Sensitive Spelling Correction"):
  with members:
    - "Mistake-Driven Learning in Text Categorization"
    - "Learning Distributions from Random Walks"

Connected through hyperedge HE-2 (induced by source paper "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"):
  with members:
    - "Query by Committee"

Connected through hyperedge HE-3 (induced by source paper "Query by Committee"):
  with members:
    - "Sifting informative examples from a random source."
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "On-Site Learning"

Connected through hyperedge HE-4 (induced by source paper "Improving Generalization with Active Learning"):
  with members:
    - "Information-based objective functions for active data selection"

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