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 Probabilistic Calculus of Actions"
  Abstract: We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yjX = x), which represents the observation X = x, and causal conditioning, P (yjdo(X = x)), read: the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions and observations.

Connected through hyperedge HE-1 (induced by source paper "Probabilistic evaluation of sequential plans from causal models with hidden variables"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "Causal diagrams for empirical research"
    - "Axioms of Causal Relevance"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"

Connected through hyperedge HE-2 (induced by source paper "Counterfactuals and Policy Analysis in Structural Models"):
  with members:
    - "Probabilistic evaluation of counterfactual queries"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-3 (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"
    - "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"
    - "Regression Can Build Predictive Causal Models"

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