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 Qualitative Markov Assumption and Its Implications for Belief Change"
  Abstract: The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while update treats a surprising observation as an indication that the world has changed. In general, we would expect that an agent making an observation may both want to revise some earlier beliefs and assume that some change has occurred in the world. We define a novel approach to belief change that allows us to do this, by applying ideas from probability theory in a qualitative settings. The key idea is to use a qualitative Markov assumption, which says that state transitions are independent. We show that a recent approach to modeling qualitative uncertainty using plausibility measures allows us to make such a qualitative Markov assumption in a relatively straightforward way, and show how the Markov assumption can be used to provide an attractive belief-change model.

Connected through hyperedge HE-1 (induced by source paper "Plausibility Measures: A User's Guide"):
  with members:
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-2 (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."
    - "Plausibility Measures: A User's Guide"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

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"
    - "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-4 (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"
    - "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"
    - "Iterated Revision and Minimal Change of Conditional Beliefs"

Connected through hyperedge HE-5 (induced by source paper "Modeling Belief in Dynamic Systems. Part I: Foundations"):
  with members:
    - "A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL"

Connected through hyperedge HE-6 (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"
    - "Plausibility Measures: A User's Guide"
    - "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