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: "Oblivious Decision Trees, Graphs, and Top-Down Pruning"
  Abstract: We describe a supervised learning algorithm, EODG, that uses mutual information to build an oblivious decision tree. The tree is then converted to an Oblivious read-Once Decision Graph (OODG) by merging nodes at the same level of the tree. For domains that are appropriate for both decision trees and OODGs, performance is approximately the same as that of C4.5, but the number of nodes in the OODG is much smaller. The merging phase that converts the oblivious decision tree to an OODG provides a new way of dealing with the replication problem and a new pruning mechanism that works top down starting from the root. The pruning mechanism is well suited for finding symmetries and aids in recovering from splits on irrelevant features that may happen during the tree construction.

Connected through hyperedge HE-1 (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"
    - "Data Mining using MLC A Machine Learning Library in C http://www.sgi.com/Technology/mlc"

Connected through hyperedge HE-2 (induced by source paper "On the Induction of Intelligible Ensembles"):
  with members:
    - "On Pruning and Averaging Decision Trees"
    - "Experiments with a New Boosting Algorithm"
    - "Estimating the Accuracy of Learned Concepts"

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