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: "Supervised and Unsupervised Discretization of Continuous Features"
  Abstract: Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm significantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm significantly improved if features were discretized in advance; in our experiments, the performance never significantly degraded, an interesting phenomenon considering the fact that C4.5 is capable of locally discretiz ing features.

Connected through hyperedge HE-1 (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"
    - "Targeting Business Users with Decision Table Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Machine Learning and Inference"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"

Connected through hyperedge HE-3 (induced by source paper "BOOSTING AND NAIVE BAYESIAN LEARNING"):
  with members:
    - "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Building Classifiers using Bayesian Networks"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

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