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: "Bayesian Graphical Modeling for Intelligent Tutoring Systems"
  Abstract: Conventional Intelligent Tutoring Systems (ITS) do not acknowledge uncertainty about the student's knowledge. Yet, both the outcome of any teaching intervention and the exact state of the student's knowledge are uncertain. In recent years, researchers have made startling progress in the management of uncertainty in knowledge-based systems. Building on these developments, we describe an ITS architecture that explicitly models uncertainty. This will facilitate more accurate student modeling and provide ITS's which can learn.

Connected through hyperedge HE-1 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "The out-of-bootstrap method for model averaging and selection"
    - "Stacked Density Estimation"
    - "Bayesian Graphical Models for Discrete Data"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Parallel Markov chain Monte Carlo sampling."

Connected through hyperedge HE-2 (induced by source paper "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"):
  with members:
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Abstract"
    - "The Automated Mapping of Plans for Plan Recognition"

Connected through hyperedge HE-3 (induced by source paper "Bayesian Graphical Models for Discrete Data"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Decomposable graphical Gaussian model determination"
    - "Statistical Ideas for Selecting Network Architectures"

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