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: "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"
  Abstract: One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.

Connected through hyperedge HE-1 (induced by source paper "Combining Data Mining and Machine Learning for Effective User Profiling"):
  with members:
    - "Learning Decision Lists Using Homogeneous Rules"

Connected through hyperedge HE-2 (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"
    - "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: Rule_Learning