human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Model Selection and Accounting for Model Uncertainty in Linear Regression Models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Bayesian inference for nondecomposable graphical Gaussian models")
  Member "A Probabilistic Calculus of Actions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Probabilistic evaluation of sequential plans from causal models with hidden variables")
  Member "On the Testability of Causal Models with Latent and Instrumental Variables":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Causal diagrams for empirical research")
  Member "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Probabilistic evaluation of counterfactual queries")
  Member "Probabilistic evaluation of counterfactual queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY")
  Member "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Causal diagrams for empirical research")
  Member "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks")
  Member "Two Algorithms for Inducing Structural Equation Models from Data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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