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: "On Pruning and Averaging Decision Trees"
  Abstract: Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computa-tionally efficient method of averaging, namely averaging over the extended fanned set of a tree. Since there are a wide range of approaches to pruning, we compare tree averaging with a traditional pruning approach, along with an optimal pruning approach.

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"
    - "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 "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Multivariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"

Connected through hyperedge HE-3 (induced by source paper "On the Induction of Intelligible Ensembles"):
  with members:
    - "Experiments with a New Boosting Algorithm"
    - "Estimating the Accuracy of Learned Concepts"
    - "Oblivious Decision Trees, Graphs, and Top-Down Pruning"

Connected through hyperedge HE-4 (induced by source paper "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Finding Overlapping Distributions with MML"
    - "Unsupervised Learning Using MML"
    - "BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER"
    - "SINGLE FACTOR ANALYSIS BY MML ESTIMATION"
    - "Bayesian and Information-Theoretic Priors for Bayesian Network Parameters"
    - "Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data"

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