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 THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
  Abstract: This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we ad dress the issue of non-temporal causation.

Connected through hyperedge HE-1 (induced by source paper "A Probabilistic Calculus of Actions"):
  with members:
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "Counterfactuals and Policy Analysis in Structural Models"

Connected through hyperedge HE-2 (induced by source paper "A Parallel Learning Algorithm for Bayesian Inference Networks"):
  with members:
    - "Theory Refinement on Bayesian Networks"

Connected through hyperedge HE-3 (induced by source paper "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"):
  with members:
    - "Two Algorithms for Inducing Structural Equation Models from Data"
    - "Regression Can Build Predictive Causal Models"
    - "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"

Connected through hyperedge HE-4 (induced by source paper "Probabilistic evaluation of counterfactual queries"):
  with members:
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "Decision-Theoretic Foundations for Causal Reasoning"
    - "Counterfactuals and Policy Analysis in Structural Models"

Connected through hyperedge HE-5 (induced by source paper "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"):
  with members:
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "Causal diagrams for empirical research"
    - "Causal Inference from Indirect Experiments"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"

Connected through hyperedge HE-6 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "The out-of-bootstrap method for model averaging and selection"
    - "Stacked Density Estimation"
    - "Bayesian Graphical Models for Discrete Data"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Parallel Markov chain Monte Carlo sampling."

Connected through hyperedge HE-7 (induced by source paper "An Algorithm for the Construction of Bayesian Network Structures from Data"):
  with members:
    - "Learning Bayesian Networks from Incomplete Data"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Learning Belief Networks from Data: An Information Theory Based Approach"
    - "An Algorithm for Bayesian Belief Network Construction from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"

Connected through hyperedge HE-8 (induced by source paper "Two Algorithms for Inducing Structural Equation Models from Data"):
  with members:
    - "Regression Can Build Predictive Causal Models"
    - "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"
    - "A Statistical Semantics for Causation Key words: causality, induction, learning"

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