human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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

Connected through hyperedge HE-1 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "On Pruning and Averaging Decision Trees"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "Adaptive Mixtures of Probabilistic Transducers"
    - "Using and combining predictors that specialize"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"

Connected through hyperedge HE-2 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"):
  with members:
    - "Error Reduction through Learning Multiple Descriptions"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Learning Trees and Rules with Set-valued Features"
    - "Mistake-Driven Learning in Text Categorization"
    - "Using and combining predictors that specialize"
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-4 (induced by source paper "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"

Connected through hyperedge HE-5 (induced by source paper "Pruning Adaptive Boosting ICML-97 Final Draft"):
  with members:
    - "Experiments with a New Boosting Algorithm"

Connected through hyperedge HE-6 (induced by source paper "An Efficient Boosting Algorithm for Combining Preferences"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "Learning to Order Things"
    - "Improved Boosting Algorithms Using Confidence-rated Predictions"

Connected through hyperedge HE-7 (induced by source paper "Actively Searching for an Effective Neural-Network Ensemble"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "Learning from Bad Data"

Connected through hyperedge HE-8 (induced by source paper "Learning Sparse Perceptrons"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

Question: Please tell me which class the center node 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