human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Targeting Business Users with Decision Table Classifiers"):
  Abstract: Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible.

Includes members:
  Member "The Power of Decision Tables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Decision Tree Induction Based on Efficient Tree Restructuring")
  Member "Error-Based and Entropy-Based Discretization of Continuous Features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "MLC A Machine Learning Library in C")
  Member "Oblivious Decision Trees, Graphs, and Top-Down Pruning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On the Induction of Intelligible Ensembles")
  Member "Data Mining using MLC A Machine Learning Library in C http://www.sgi.com/Technology/mlc":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Pruning Decision Trees with Misclassification Costs")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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: Probabilistic_Methods