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 Complexity of Conditional Logics"
  Abstract: Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants discussed in the literature. We show that, in general, deciding satisfiability is PSPACE-complete for formulas with arbitrary conditional nesting and NP-complete for formulas with bounded nesting of conditionals. However, we provide several exceptions to this rule. Of particular note are results showing that (a) when assuming uniformity (i.e., that all worlds agree on what worlds are possible), the decision problem becomes EXPTIME-complete even for formulas with bounded nesting, and (b) when assuming absoluteness (i.e., that all worlds agree on all conditional statements), the decision problem is NP-complete for for mulas with arbitrary nesting.

Connected through hyperedge HE-1 (induced by source paper "Plausibility Measures and Default Reasoning"):
  with members:
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "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-2 (induced by source paper "Updates and Counterfactuals"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "A Knowledge-Based Framework for Belief Change Part I: Foundations"):
  with members:
    - "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 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 "Defining Relative Likelihood in Partially-Ordered Preferential Structures"):
  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-5 (induced by source paper "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."):
  with members:
    - "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"
    - "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"

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