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: "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
  Abstract: In this paper we present an average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. One goal of research in machine learning is to discover principles that relate algorithms and domain characteristics to behavior. To this end, many researchers have carried out systematic experimentation with natural and artificial domains in search of empirical regularities (e.g., Kibler & Langley, 1988). Others have focused on theoretical analyses, often within the paradigm of probably approximately correct learning (e.g., Haus-sler, 1990). However, most experimental studies are based only on informal analyses of the learning task, whereas most formal analyses address the worst case, and thus bear little relation to empirical results. ber of attributes, and the class and attribute frequencies, they obtain predictions about the behavior of induction algorithms and used experiments to check their analyses. 1 However, their research does not focus on algorithms typically used by the experimental and practical sides of machine learning, and it is important that average-case analyses be extended to such methods. Recently, there has been growing interest in probabilistic approaches to inductive learning. For example, Fisher (1987) has described Cobweb, an incremental algorithm for conceptual clustering that draws heavily on Bayesian ideas, and the literature reports a number of systems that build on this work (e.g., Allen & Lang-ley, 1990; Iba & Gennari, 1991; Thompson & Langley, 1991). Cheeseman et al. (1988) have outlined Auto-Class, a nonincremental system that uses Bayesian methods to cluster instances into groups, and other researchers have focused on the induction of Bayesian inference networks (e.g., Cooper & Kerskovits, 1991). These recent Bayesian learning algorithms are complex and not easily amenable to analysis, but they share a common ancestor that is simpler and more tractable. This supervised algorithm, which we refer to simply as a Bayesian classifier, comes originally from work in pattern recognition (Duda & Hart, 1973). The method stores a probabilistic summary for each class; this summary contains the conditional probability of each attribute value given the class, as well as the probability (or base rate) of the class. This data structure approximates the representational power of a perceptron; it describes a single decision boundary through the instance space. When the algorithm encounters a new instance, it updates the probabilities stored with the specified class. Neither the order of training instances nor the occurrence of classification errors have any effect on this process. When given a test instance, the classifier uses an evaluation function (which we describe in detail later) to rank the alter

Connected through hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"
    - "Logarithmic-Time Updates and Queries in Probabilistic Networks"

Connected through hyperedge HE-2 (induced by source paper "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"):
  with members:
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"

Connected through hyperedge HE-3 (induced by source paper "Average-Case Analysis of a Nearest Neighbor Algorithm"):
  with members:
    - "Oblivious Decision Trees and Abstract Cases"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-4 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "On learning hierarchical classifications"
    - "Model selection using measure functions"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "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-5 (induced by source paper "Mining and Model Simplicity: A Case Study in Diagnosis"):
  with members:
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-6 (induced by source paper "Induction of One-Level Decision Trees"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"

Connected through hyperedge HE-7 (induced by source paper "Computational Learning in Humans and Machines"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"

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