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: "Plausibility Measures: A User's Guide"
  Abstract: We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an as needed basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their algebraic properties, analogues to the use of + and fi in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool.

Connected through hyperedge HE-1 (induced by source paper "Plausibility Measures and Default Reasoning"):
  with members:
    - "On the Complexity of Conditional Logics"
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

Connected through hyperedge HE-2 (induced by source paper "A Qualitative Markov Assumption and Its Implications for Belief Change"):
  with members:
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "Modeling Belief in Dynamic Systems. Part I: Foundations"
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-3 (induced by source paper "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."):
  with members:
    - "On the Complexity of Conditional Logics"
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"
    - "Abduction as Belief Revision"
    - "Abduction to Plausible Causes: An Event-based Model of Belief Update"
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "Plausibility Measures and Default Reasoning"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

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