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 "Decision-Theoretic Foundations for Causal Reasoning"):
  Abstract: We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

Includes members:
  Member "Causal diagrams for empirical research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Causal Inference from Indirect Experiments")
  Member "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Probabilistic Calculus of Actions")
  Member "Probabilistic evaluation of counterfactual queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY")
  Member "Defining Explanation in Probabilistic Systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Causal diagrams for empirical research")

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