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: "On the Logic of Iterated Belief Revision"
  Abstract: We show in this paper that the AGM postulates are too week to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a belief set. We also show that a simple modification to the AGM framework can allow belief revision to be a function of epistemic states. We establish a model-based representation theorem which characterizes the proposed postulates and constrains, in turn, the way in which entrenchment orderings may be transformed under iterated belief revision.

Connected through hyperedge HE-1 (induced by source paper "Belief Revision: A Critique"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Iterated Revision and Minimal Change of Conditional Beliefs"):
  with members:
    - "Generalized Update: Belief Change in Dynamic Settings"

Connected through hyperedge HE-3 (induced by source paper "A Knowledge-Based Framework for Belief Change Part I: Foundations"):
  with members:
    - "On the Complexity of Conditional Logics"
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "Abduction to Plausible Causes: An Event-based Model of Belief Update"
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "On the Semantics of Belief Revision Systems"
    - "A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL"

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