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 New Approach for Induction: From a Non-Axiomatic Logical Point of View"
  Abstract: Non-Axiomatic Reasoning System (NARS) is designed to be a general-purpose intelligent reasoning system, which is adaptive and works under insufficient knowledge and resources. This paper focuses on the components of NARS that contribute to the system's induction capacity, and shows how the traditional problems in induction are addressed by the system. The NARS approach of induction uses an term-oriented formal language with an experience-grounded semantics that consistently interprets various types of uncertainty. An induction rule generates conclusions from common instance of terms, and a revision rule combines evidence from different sources. In NARS, induction and other types of inference, such as deduction and abduction, are based on the same semantic foundation, and they cooperate in inference activities of the system. The system's control mechanism makes knowledge-driven, context-dependent inference possible.

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,"
    - "Belief Revision in Probability Theory"
    - "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"

Connected through hyperedge HE-2 (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"
    - "A Unified Treatment of Uncertainties"
    - "Heuristics and Normative Models of Judgment under Uncertainty"
    - "Reference Classes and Multiple Inheritances"
    - "A Defect in Dempster-Shafer Theory"

Connected through hyperedge HE-3 (induced by source paper "Reference Classes and Multiple Inheritances"):
  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"
    - "Heuristics and Normative Models of Judgment under Uncertainty"

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