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: "Abstract"
  Abstract: Automated decision making is often complicated by the complexity of the knowledge involved. Much of this complexity arises from the context-sensitive variations of the underlying phenomena. We propose a framework for representing descriptive, context-sensitive knowledge. Our approach attempts to integrate categorical and uncertain knowledge in a network formalism. This paper outlines the basic representation constructs, examines their expressiveness and efficiency, and discusses the potential applications of the framework.

Connected through hyperedge HE-1 (induced by source paper "Representation Requirements for Supporting Decision Model Formulation"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"):
  with members:
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"
    - "The Automated Mapping of Plans for Plan Recognition"

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