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: "Learning Decision Lists Using Homogeneous Rules"
  Abstract: A decision list is an ordered list of conjunctive rules (?). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is learned, the training examples that match the rule are removed from the training set. We propose a novel solution to the problem: composing decision lists from homogeneous rules, rules whose classification accuracy does not change with their position in the decision list. We prove that the problem of finding a maximally accurate decision list can be reduced to the problem of finding maximally accurate homogeneous rules. We report on the performance of our algorithm on data sets from the UCI repository and on the MONK's problems.

Connected through hyperedge HE-1 (induced by source paper "Combining Data Mining and Machine Learning for Effective User Profiling"):
  with members:
    - "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"

Connected through hyperedge HE-2 (induced by source paper "Stochastically Guided Disjunctive Version Space Learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Error Reduction through Learning Multiple Descriptions"
    - "Classifiers: A Theoretical and Empirical Study"

Connected through hyperedge HE-3 (induced by source paper "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"):
  with members:
    - "Lookahead and Pathology in Decision Tree Induction"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"
    - "What should be minimized in a decision tree: A re-examination"
    - "Bias and the Quantification of Stability Bias and the Quantification of Stability Bias and the"

Connected through hyperedge HE-4 (induced by source paper "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"):
  with members:
    - "Combining Data Mining and Machine Learning for Effective User Profiling"

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