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: "Toward a Market Model for Bayesian Inference"
  Abstract: We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities represented by the Bayesian network. A computational market model such as this may provide a useful framework for investigations of belief aggregation, distributed probabilistic inference, resource allocation under uncertainty, and other problems of de centralized uncertainty.

Connected through hyperedge HE-1 (induced by source paper "A Market Framework for Pooling Opinions"):
  with members:
    - "Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market"

Connected through hyperedge HE-2 (induced by source paper "Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market"):
  with members:
    - "A Market Framework for Pooling Opinions"

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