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: "A Parallel Learning Algorithm for Bayesian Inference Networks"
  Abstract: We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric, and a distributed, asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, effectiveness, and scalability of our approach empirically with several experiments using on the order of 20 machines. More specifically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables.

Connected through hyperedge HE-1 (induced by source paper "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "Probabilistic evaluation of counterfactual queries"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Two Algorithms for Inducing Structural Equation Models from Data"

Connected through hyperedge HE-2 (induced by source paper "Theory Refinement on Bayesian Networks"):
  with members:
    (none)

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