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: "Belief Revision in Probability Theory"
  Abstract: In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments are properly distinguished, it follows that Bayes' theorem is not a generally applicable revision rule. Upon properly distinguishing belief revision from belief updating, we see that Jeffrey's rule and its variations are not revision rules, either. Without these distinctions, the limitation of the Bayesian approach is often ignored or underestimated. Revision, in its general form, cannot be done in the Bayesian approach, because a probability distribution function alone does not contain the information needed by the operation.

Connected through hyperedge HE-1 (induced by source paper "From Inheritance Relation to Non-Axiomatic Logic"):
  with members:
    - "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"
    - "A Unified Treatment of Uncertainties"
    - "Heuristics and Normative Models of Judgment under Uncertainty"
    - "Reference Classes and Multiple Inheritances"
    - "A Defect in Dempster-Shafer Theory"
    - "A New Approach for Induction: From a Non-Axiomatic Logical Point of View"

Connected through hyperedge HE-2 (induced by source paper "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"):
  with members:
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"
    - "A Unified Treatment of Uncertainties"

Connected through hyperedge HE-3 (induced by source paper "Reference Classes and Multiple Inheritances"):
  with members:
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"
    - "Heuristics and Normative Models of Judgment under Uncertainty"
    - "A New Approach for Induction: From a Non-Axiomatic Logical Point of View"

Connected through hyperedge HE-4 (induced by source paper "Heuristics and Normative Models of Judgment under Uncertainty"):
  with members:
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"
    - "Reference Classes and Multiple Inheritances"

Connected through hyperedge HE-5 (induced by source paper "A Unified Treatment of Uncertainties"):
  with members:
    - "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"
    - "A Defect in Dempster-Shafer Theory"

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