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: "Abduction as Belief Revision"
  Abstract: We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It also illustrates how more general information can be incorporated in a principled manner. fl Some parts of this paper appeared in preliminary form as Abduction as Belief Revision: A Model of Preferred Explanations, Proc. of Eleventh National Conf. on Artificial Intelligence (AAAI-93), Washington, DC, pp.642-648 (1993).

Connected through hyperedge HE-1 (induced by source paper "Explaining Predictions in Bayesian Networks and Influence Diagrams"):
  with members:
    - "Defining Explanation in Probabilistic Systems"

Connected through hyperedge HE-2 (induced by source paper "Generalized Update: Belief Change in Dynamic Settings"):
  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."
    - "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"

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 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-4 (induced by source paper "Abduction to Plausible Causes: An Event-based Model of Belief Update"):
  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."

Connected through hyperedge HE-5 (induced by source paper "Defining Explanation in Probabilistic Systems"):
  with members:
    - "Causal diagrams for empirical research"
    - "Explaining Predictions in Bayesian Networks and Influence Diagrams"
    - "Decision-Theoretic Foundations for Causal Reasoning"

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