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 "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  Abstract: We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth [10] can be adapted to this model yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in R n

Includes members:
  Member "Learning Sparse Perceptrons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Represent Codons: A Challenge Problem for Constructive Induction")
  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 "An Efficient Boosting Algorithm for Combining Preferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods")
  Member "BOOSTING AND NAIVE BAYESIAN LEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Building Classifiers using Bayesian Networks")
  Member "Actively Searching for an Effective Neural-Network Ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Learning to Order Things":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "An Efficient Boosting Algorithm for Combining Preferences")
  Member "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms")
  Member "Context-sensitive learning methods for text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Using and combining predictors that specialize")

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