human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Improving Generalization with Active Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection")
  Member "Query by Committee":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On-Site Learning")
  Member "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Query by Committee")
  Member "Applying Winnow to Context-Sensitive Spelling Correction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning Distributions from Random Walks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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