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: "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
  Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions.

Connected through hyperedge HE-1 (induced by source paper "On the Complexity of Conditional Logics"):
  with members:
    - "Updates and Counterfactuals"
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"
    - "Plausibility Measures and Default Reasoning"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

Connected through hyperedge HE-2 (induced by source paper "On the Semantics of Belief Revision Systems"):
  with members:
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"
    - "Rational Belief Revision (Preliminary Report)"

Connected through hyperedge HE-3 (induced by source paper "Plausibility Measures: A User's Guide"):
  with members:
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-4 (induced by source paper "Plausibility Measures and Default Reasoning"):
  with members:
    - "On the Complexity of Conditional Logics"
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "Plausibility Measures: A User's Guide"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

Connected through hyperedge HE-5 (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"
    - "Abduction to Plausible Causes: An Event-based Model of Belief Update"
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"
    - "On the Logic of Iterated Belief Revision"
    - "On the Semantics of Belief Revision Systems"
    - "A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL"

Connected through hyperedge HE-6 (induced by source paper "Abduction as Belief Revision"):
  with members:
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "Abduction to Plausible Causes: An Event-based Model of Belief Update"
    - "Explaining Predictions in Bayesian Networks and Influence Diagrams"
    - "Defining Explanation in Probabilistic Systems"

Connected through hyperedge HE-7 (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"
    - "Modeling Belief in Dynamic Systems. Part I: Foundations"
    - "Plausibility Measures: A User's Guide"
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-8 (induced by source paper "Generalized Update: Belief Change in Dynamic Settings"):
  with members:
    - "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"
    - "Iterated Revision and Minimal Change of Conditional Beliefs"
    - "A Qualitative Markov Assumption and Its Implications for Belief Change"

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