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: "Pessimistic decision tree pruning based on tree size"
  Abstract: In this work we develop a new criteria to perform pessimistic decision tree pruning. Our method is theoretically sound and is based on theoretical concepts such as uniform convergence and the Vapnik-Chervonenkis dimension. We show that our criteria is very well motivated, from the theory side, and performs very well in practice. The accuracy of the new criteria is comparable to that of the current method used in C4.5.

Connected through hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "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"
    - "Learning Classification Trees"

Connected through hyperedge HE-2 (induced by source paper "Self bounding learning algorithms"):
  with members:
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"

Connected through hyperedge HE-3 (induced by source paper "Statistical Tests for Comparing Supervised Classification Learning Algorithms"):
  with members:
    - "Back Propagation is Sensitive to Initial Conditions"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-4 (induced by source paper "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"):
  with members:
    - "Lazy Bayesian Trees"

Connected through hyperedge HE-5 (induced by source paper "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"):
  with members:
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "On the Boosting Ability of Top-Down Decision Tree Learning Algorithms provably optimal for decision tree"

Connected through hyperedge HE-6 (induced by source paper "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"):
  with members:
    - "Learning Active Classifiers"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"

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