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: "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.

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"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "On Pruning and Averaging 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 "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-3 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

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