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: "Induction of Selective Bayesian Classifiers"
  Abstract: In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.

Connected through hyperedge HE-1 (induced by source paper "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"):
  with members:
    - "Learning Limited Dependence Bayesian Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"):
  with members:
    - "Context-sensitive attribute estimation in regression"
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Learning One More Thing"
    - "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Learning in neural networks with Bayesian prototypes"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

Connected through hyperedge HE-4 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers"):
  with members:
    - "Toward Optimal Feature Selection"
    - "A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-5 (induced by source paper "Learning Bayesian Prototype Trees by Simulated Annealing"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning in neural networks with Bayesian prototypes"

Connected through hyperedge HE-6 (induced by source paper "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

Connected through hyperedge HE-7 (induced by source paper "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"

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: Probabilistic_Methods