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 using MLC A Machine Learning Library in C http://www.sgi.com/Technology/mlc"
  Abstract: Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC ++ , which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. MLC ++ not only provides a workbench for such comparisons, but also provides a library of C ++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.

Connected through hyperedge HE-1 (induced by source paper "Pruning Decision Trees with Misclassification Costs"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Targeting Business Users with Decision Table Classifiers"):
  with members:
    - "The Power of Decision Tables"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Oblivious Decision Trees, Graphs, and Top-Down Pruning"

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: Theory