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: "Probabilistic evaluation of sequential plans from causal models with hidden variables"
  Abstract: The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a given plan can be predicted from passive observations on measured variables only. When the criterion is satisfied, a closed-form expression is provided for the probability that the plan will achieve a specified goal.

Connected through hyperedge HE-1 (induced by source paper "A Probabilistic Calculus of Actions"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Counterfactuals and Policy Analysis in Structural Models"

Connected through hyperedge HE-2 (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"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "Defining Explanation in Probabilistic Systems"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "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-3 (induced by source paper "Axioms of Causal Relevance"):
  with members:
    - "On the Testability of Causal Models with Latent and Instrumental Variables"

Connected through hyperedge HE-4 (induced by source paper "The New Challenge: From a Century of Statistics to an Age of Causation"):
  with members:
    - "Causal diagrams for empirical research"
    - "Causal Inference from Indirect Experiments"

Connected through hyperedge HE-5 (induced by source paper "On the Testability of Causal Models with Latent and Instrumental Variables"):
  with members:
    - "Causal diagrams for empirical research"
    - "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"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"

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