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: "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
  Abstract: This paper demonstrates the use of graphs as a mathematical tool for expressing independencies, and as a formal language for communicating and processing causal information for decision 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 theory of Bayesian networks 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. As an example, we show how the effect of smoking on lung cancer can be quantified from non-experimental data, using a minimal set of qualitative assumptions. 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. fl Portions of this paper were presented at the 49th Session of the International Statistical Institute, Florence, Italy, August 25 - September 3, 1993.

Connected through hyperedge HE-1 (induced by source paper "Causal diagrams for empirical research"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "Causal Inference from Indirect Experiments"
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "Defining Explanation in Probabilistic Systems"
    - "Decision-Theoretic Foundations for Causal Reasoning"
    - "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-2 (induced by source paper "Causal Inference from Indirect Experiments"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "Causal diagrams for empirical research"
    - "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"

Connected through hyperedge HE-3 (induced by source paper ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"):
  with members:
    - "An Alternative Markov Property for Chain Graphs"
    - "Bayesian Graphical Models for Discrete Data"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "Bayesian inference for nondecomposable graphical Gaussian models"

Connected through hyperedge HE-4 (induced by source paper "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"):
  with members:
    - "Causal Inference from Indirect Experiments"

Connected through hyperedge HE-5 (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"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "Probabilistic evaluation of counterfactual queries"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "On the Testability of Causal Models with Latent and Instrumental Variables"):
  with members:
    - "Causal diagrams for empirical research"
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "Axioms of Causal Relevance"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"

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