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: "Combining Data Mining and Machine Learning for Effective User Profiling"
  Abstract: This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rule- learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.

Connected through hyperedge HE-1 (induced by source paper "Learning Decision Lists Using Homogeneous Rules"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"
    - "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"

Connected through hyperedge HE-2 (induced by source paper "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"):
  with members:
    - "Learning Decision Lists Using Homogeneous Rules"

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