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: "On learning hierarchical classifications"
  Abstract: Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate multi-category classifiers for these domains. First, we consider the possibility of exploiting a class hierarchy as prior knowledge that can help one learn a more accurate classifier. We explore the benefits of learning category-discriminants in a hard top-down fashion and compare this to a soft approach which shares training data among sibling categories. In doing so, we verify that hierarchies have the potential to improve prediction accuracy. But we argue that the reasons for this can be subtle. Sometimes, the improvement is only because using a hierarchy happens to constrain the expressiveness of a hypothesis class in an appropriate manner. However, various controlled experiments show that in other cases the performance advantage associated with using a hierarchy really does seem to be due to the prior knowledge it encodes.

Connected through hyperedge HE-1 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "Model selection using measure functions"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"
    - "MLC A Machine Learning Library in C"

Connected through hyperedge HE-2 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"):
  with members:
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Lazy Bayesian Trees"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-3 (induced by source paper "Bias Plus Variance Decomposition for Zero-One Loss Functions"):
  with members:
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Characterizing the generalization performance of model selection strategies"
    - "Error-Correcting Output Codes for Local Learners"
    - "Bias, variance and prediction error for classification rules"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

Connected through hyperedge HE-4 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Factorial Learning and the EM Algorithm"

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