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: "The Power of Decision Tables"
  Abstract: We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time.

Connected through hyperedge HE-1 (induced by source paper "Targeting Business Users with Decision Table Classifiers"):
  with members:
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Oblivious Decision Trees, Graphs, and Top-Down Pruning"
    - "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 "Decision Tree Induction Based on Efficient Tree Restructuring"):
  with members:
    - "An Improved Algorithm for Incremental Induction of Decision Trees"

Connected through hyperedge HE-3 (induced by source paper "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"

Connected through hyperedge HE-4 (induced by source paper "Induction of Condensed Determinations"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Oblivious Decision Trees and Abstract Cases"

Connected through hyperedge HE-5 (induced by source paper "A Yardstick for the Evaluation of Case-Based Classifiers"):
  with members:
    - "Formalising the knowledge content of case memory systems"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"

Connected through hyperedge HE-6 (induced by source paper "Automatic Parameter Selection by Minimizing Estimated Error"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "Learning Symbolic Rules Using Artificial Neural Networks"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

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