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 Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
  Abstract: In this work, we present a new bottom-up algorithm for decision tree pruning that is very efficient (requiring only a single pass through the given tree), and prove a strong performance guarantee for the generalization error of the resulting pruned tree. We work in the typical setting in which the given tree T may have been derived from the given training sample S, and thus may badly overfit S. In this setting, we give bounds on the amount of additional generalization error that our pruning suffers compared to the optimal pruning of T . More generally, our results show that if there is a pruning of T with small error, and whose size is small compared to jSj, then our algorithm will find a pruning whose error is not much larger. This style of result has been called an index of resolvability result by Barron and Cover in the context of density estimation. A novel feature of our algorithm is its locality | the decision to prune a subtree is based entirely on properties of that subtree and the sample reaching it. To analyze our algorithm, we develop tools of local uniform convergence, a generalization of the standard notion that may prove useful in other settings.

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"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "Adaptive Mixtures of Probabilistic Transducers"
    - "Using and combining predictors that specialize"

Connected through hyperedge HE-2 (induced by source paper "On the Boosting Ability of Top-Down Decision Tree Learning Algorithms provably optimal for decision tree"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"):
  with members:
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Toward Efficient Agnostic Learning"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"
    - "Preventing "Overfitting" of Cross-Validation Data"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"

Connected through hyperedge HE-4 (induced by source paper "Pessimistic decision tree pruning based on tree size"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"

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