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 "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"):
  Abstract: This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We show how complex information about external interventions can be organized and represented graphically and, conversely, how the graphical representation can be used to facilitate quantitative predictions of the effects of interventions. We first review the Markovian account of causation and show that directed acyclic graphs (DAGs) offer an economical scheme for representing conditional independence assumptions and for deducing and displaying all the logical consequences of such assumptions. We then introduce the manipulative account of causation and show that any DAG defines a simple transformation which tells us how the probability distribution will change as a result of external interventions in the system. Using this transformation it is possible to quantify, from non-experimental data, the effects of external interventions and to specify conditions under which randomized experiments are not necessary. Finally, the paper offers a graphical interpretation for Rubin's model of causal effects, and demonstrates its equivalence to the manipulative account of causation. We exemplify the tradeoffs between the two approaches by deriving nonparametric bounds on treatment effects under conditions of imperfect compliance.

Includes members:
  Member ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Bayesian inference for nondecomposable graphical Gaussian models")
  Member "On the Testability of Causal Models with Latent and Instrumental Variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Causal diagrams for empirical research")
  Member "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Probabilistic Calculus of Actions")
  Member "Probabilistic evaluation of counterfactual queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Counterfactuals and Policy Analysis in Structural Models")

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