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: "Context-sensitive learning methods for text categorization"
  Abstract: Two recently implemented machine learning algorithms, RIPPER and sleeping experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the "context" of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.

Connected through hyperedge HE-1 (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"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"

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"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-3 (induced by source paper "Learning Trees and Rules with Set-valued Features"):
  with members:
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Transferring and Retraining Learned Information Filters"
    - "Inverse entailment and Progol"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-4 (induced by source paper "Transferring and Retraining Learned Information Filters"):
  with members:
    - "Incremental Reduced Error Pruning"
    - "Learning Trees and Rules with Set-valued Features"
    - "Feature Generation for Sequence Categorization"
    - "Is Learning The n-th Thing Any Easier Than Learning The First?"

Connected through hyperedge HE-5 (induced by source paper "Mistake-Driven Learning in Text Categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Applying Winnow to Context-Sensitive Spelling Correction"

Connected through hyperedge HE-6 (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"
    - "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"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

Connected through hyperedge HE-7 (induced by source paper "Heterogeneous Uncertainty Sampling for Supervised Learning"):
  with members:
    - "More Efficient Windowing"
    - "Information-based objective functions for active data selection"
    - "Learning Trees and Rules with Set-valued Features"
    - "Query by Committee"

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