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: "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
  Abstract: Many algorithms for inferring a decision tree from data involve a two-phase process: First, a very large decision tree is grown which typically ends up "over-fitting" the data. To reduce over-fitting, in the second phase, the tree is pruned using one of a number of available methods. The final tree is then output and used for classification on test data. In this paper, we suggest an alternative approach to the pruning phase. Using a given unpruned decision tree, we present a new method of making predictions on test data, and we prove that our algorithm's performance will not be "much worse" (in a precise technical sense) than the predictions made by the best reasonably small pruning of the given decision tree. Thus, our procedure is guaranteed to be competitive (in terms of the quality of its predictions) with any pruning algorithm. We prove that our procedure is very efficient and highly robust. Our method can be viewed as a synthesis of two previously studied techniques. First, we apply Cesa-Bianchi et al.'s [4] results on predicting using "expert advice" (where we view each pruning as an "expert") to obtain an algorithm that has provably low prediction loss, but that is com-putationally infeasible. Next, we generalize and apply a method developed by Buntine [3], [2] and Willems, Shtarkov and Tjalkens [20], [21] to derive a very efficient implementation of this procedure.

Connected through hyperedge HE-1 (induced by source paper "On Pruning and Averaging Decision Trees"):
  with members:
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "On the Induction of Intelligible Ensembles"

Connected through hyperedge HE-2 (induced by source paper "Adaptive Mixtures of Probabilistic Transducers"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Using and combining predictors that specialize"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-4 (induced by source paper "Learning Probabilistic Automata with Variable Memory Length"):
  with members:
    - "Learning Markov chains with variable memory length from noisy output"
    - "Using and combining predictors that specialize"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-5 (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-6 (induced by source paper "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "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-7 (induced by source paper "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"

Connected through hyperedge HE-8 (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"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"

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