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 "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  Abstract: We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method for parallelizing PAC learning algorithms. We provide extensions of our algorithms to cases in which the concepts are not binary and to the case where the accuracy of the learning algorithm depends on the distribution of the instances.

Includes members:
  Member "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries")
  Member "Sifting informative examples from a random source.":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Efficient Distribution-free Learning of Probabilistic Concepts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning in the Presence of Malicious Errors")
  Member "Learning From a Population of Hypotheses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs")
  Member "Game Theory, On-line Prediction and Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule")
  Member "On the Sample Complexity of Weakly Learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions")

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