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: "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.

Connected through hyperedge HE-1 (induced by source paper "Oblivious Decision Trees, Graphs, and Top-Down Pruning"):
  with members:
    - "On the Induction of Intelligible Ensembles"

Connected through hyperedge HE-2 (induced by source paper "Data Mining using MLC A Machine Learning Library in C http://www.sgi.com/Technology/mlc"):
  with members:
    - "Pruning Decision Trees with Misclassification Costs"

Connected through hyperedge HE-3 (induced by source paper "The Power of Decision Tables"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "Induction of Condensed Determinations"
    - "Decision Tree Induction Based on Efficient Tree Restructuring"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Error-Based and Entropy-Based Discretization of Continuous Features"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "MLC A Machine Learning Library in C"
    - "Supervised and Unsupervised Discretization of Continuous Features"

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: Probabilistic_Methods