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: "First Order Regression"
  Abstract: We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program Fors is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of Fors on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of Fors' performance with previous results in these domains indicates that Fors is an effective tool for ILP applications that involve numerical data.

Connected through hyperedge HE-1 (induced by source paper "First Order Regression: Applications in Real-World Domains"):
  with members:
    - "Producing More Comprehensible Models While Retaining Their Performance"

Connected through hyperedge HE-2 (induced by source paper "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "Structural Regression Trees"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Producing More Comprehensible Models While Retaining Their Performance"
    - "Context-sensitive attribute estimation in regression"
    - "The Bayesian Approach to Tree-Structured Regression"

Connected through hyperedge HE-3 (induced by source paper "Producing More Comprehensible Models While Retaining Their Performance"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "First Order Regression: Applications in Real-World Domains"
    - "Stochastic search in inductive concept learning"

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: Rule_Learning