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: "Generalized Update: Belief Change in Dynamic Settings"
  Abstract: Belief revision and belief update have been proposed as two types of belief change serving different purposes. Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world. Belief update is intended to capture changes of belief in response to a changing world. We argue that both belief revision and belief update are too restrictive; routine belief change involves elements of both. We present a model for generalized update that allows updates in response to external changes to inform the agent about its prior beliefs. This model of update combines aspects of revision and update, providing a more realistic characterization of belief change. We show that, under certain assumptions, the original update postulates are satisfied. We also demonstrate that plain revision and plain update are special cases of our model, in a way that formally verifies the intuition that revision is suitable for static belief change.

Connected through hyperedge HE-1 (induced by source paper "Iterated Revision and Minimal Change of Conditional Beliefs"):
  with members:
    - "On the Logic of Iterated Belief Revision"

Connected through hyperedge HE-2 (induced by source paper "A Knowledge-Based Framework for Belief Change Part I: Foundations"):
  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."
    - "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-3 (induced by source paper "Abduction as Belief Revision"):
  with members:
    - "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"
    - "Explaining Predictions in Bayesian Networks and Influence Diagrams"
    - "Defining Explanation in Probabilistic Systems"

Connected through hyperedge HE-4 (induced by source paper "A Qualitative Markov Assumption and Its Implications for Belief Change"):
  with members:
    - "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: A User's Guide"
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-5 (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"
    - "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: A User's Guide"
    - "Plausibility Measures and Default Reasoning"
    - "Defining Relative Likelihood in Partially-Ordered Preferential Structures"

Connected through hyperedge HE-6 (induced by source paper "Abduction to Plausible Causes: An Event-based Model of Belief Update"):
  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."

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