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: "Stochastically Guided Disjunctive Version Space Learning"
  Abstract: This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The approach focuses on combinations of confldent, possibly overlapping, concepts with an original stochastic complexity formula. The focusing is e-cient because it is organized as a simulated annealing-based beam search. The experiments show that the approach is especially suitable for developing incremental learning algorithms with the following advantages: flrst, it generates highly accurate concepts; second, it overcomes to a certain degree the sensitivity to the order of examples; and third, it handles noisy examples.

Connected through hyperedge HE-1 (induced by source paper "Error Reduction through Learning Multiple Descriptions"):
  with members:
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Classifiers: A Theoretical and Empirical Study"):
  with members:
    - "Is Consistency Harmful?"

Connected through hyperedge HE-3 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Relating Relational Learning Algorithms"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-4 (induced by source paper "Learning Decision Lists Using Homogeneous Rules"):
  with members:
    - "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"
    - "Combining Data Mining and Machine Learning for Effective User Profiling"
    - "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"

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: Rule_Learning