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: "Structural Regression Trees"
  Abstract: In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with non-determinate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems by integrating the statistical method of regression trees into ILP. SRT constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.

Connected through hyperedge HE-1 (induced by source paper "Rule-based Machine Learning Methods for Functional Prediction"):
  with members:
    - "Search-based Class Discretization"
    - "Combining estimates in regression and classification"

Connected through hyperedge HE-2 (induced by source paper "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "An adaptation of Relief for attribute estimation in regression"
    - "First Order 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 "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
    - "Specialization of Recursive Predicates"
    - "Specialization of Logic Programs by Pruning SLD-Trees"

Connected through hyperedge HE-4 (induced by source paper "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Knowledge Discovery in International Conflict Databases"
    - "Beyond Correlation: Bringing Artificial Intelligence to Events Data"

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