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 "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"):
  Abstract: We present a method for maintaining mixtures of prunings of a prediction or decision tree that extends the "node-based" prunings of [Bun90, WST95, HS97] to the larger class of edge-based prunings. The method includes an efficient online weight allocation algorithm that can be used for prediction, compression and classification. Although the set of edge-based prunings of a given tree is much larger than that of node-based prunings, our algorithm has similar space and time complexity to that of previous mixture algorithms for trees. Using the general on-line framework of Freund and Schapire [FS97], we prove that our algorithm maintains correctly the mixture weights for edge-based prunings with any bounded loss function. We also give a similar algorithm for the logarithmic loss function with a corresponding weight allocation algorithm. Finally, we describe experiments comparing node-based and edge-based mixture models for estimating the probability of the next word in English text, which show the ad vantages of edge-based models.

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 "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-2 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On Pruning and Averaging Decision Trees")

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