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: "A Unified Treatment of Uncertainties"
  Abstract: Uncertainty in artificial intelligence" is an active research field, where several approaches have been suggested and studied for dealing with various types of uncertainty. However, it's hard to rank the approaches in general, because each of them is usually aimed at a special application environment. This paper begins by defining such an environment, then show why some existing approaches cannot be used in such a situation. Then a new approach, Non-Axiomatic Reasoning System, is introduced to work in the environment. The system is designed under the assumption that the system's knowledge and resources are usually insufficient to handle the tasks imposed by its environment. The system can consistently represent several types of uncertainty, and can carry out multiple operations on these uncertainties. Finally, the new approach is compared with the previous approaches in terms of uncertainty representation and interpretation.

Connected through hyperedge HE-1 (induced by source paper "Belief Revision in Probability Theory"):
  with members:
    - "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Heuristics and Normative Models of Judgment under Uncertainty"
    - "Reference Classes and Multiple Inheritances"
    - "A Defect in Dempster-Shafer Theory"

Connected through hyperedge HE-2 (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,"
    - "Belief Revision in Probability Theory"
    - "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"
    - "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-3 (induced by source paper "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"):
  with members:
    - "Belief Revision in Probability Theory"
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"

Connected through hyperedge HE-4 (induced by source paper "Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of"):
  with members:
    - "Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,"
    - "From Inheritance Relation to Non-Axiomatic Logic"
    - "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"

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