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 "Game Theory, On-line Prediction and Boosting"):
  Abstract: We study the close connections between game theory, on-line prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the on-line prediction methods of Littlestone and War-muth. The analysis of this algorithm yields a simple proof of von Neumann's famous minmax theorem, as well as a provable method of approximately solving a game. We then show that the on-line prediction model is obtained by applying this game-playing algorithm to an appropriate choice of game and that boosting is obtained by applying the same algorithm to the dual of this game.

Includes members:
  Member "How to Use Expert Advice (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "Boosting a weak learning algorithm by majority To be published in Information and Computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts")
  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-3 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Predicting a binary sequence almost as well as the optimal biased coin":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS")

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