human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "Towards a Better Understanding of Memory-Based Reasoning Systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Logarithmic-Time Updates and Queries in Probabilistic Networks")
  Member "Induction of One-Level Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Computational Learning in Humans and Machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Semantic Grammars with Constructive Inductive Logic Programming")
  Member "Average-Case Analysis of a Nearest Neighbor Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems")
  Member "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule")
  Member "Mining and Model Simplicity: A Case Study in Diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier")
  Member "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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