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: "Employing Linear Regression in Regression Tree Leaves"
  Abstract: The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on artificial and real-life domains. The results show that the modification is beneficial as it leads to smaller classification errors of induced regression trees. Keywords: machine learning, TDIDT, regression, linear regression, Bayesian approach.

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

Connected through hyperedge HE-2 (induced by source paper "Structural Regression Trees"):
  with members:
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "Rule-based Machine Learning Methods for Functional Prediction"

Connected through hyperedge HE-3 (induced by source paper "The Bayesian Approach to Tree-Structured Regression"):
  with members:
    - "Drug design by machine learning: Modelling drug activity"

Connected through hyperedge HE-4 (induced by source paper "Context-sensitive attribute estimation in regression"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"

Connected through hyperedge HE-5 (induced by source paper "An adaptation of Relief for attribute estimation in regression"):
  with members:
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-6 (induced by source paper "Producing More Comprehensible Models While Retaining Their Performance"):
  with members:
    - "First Order Regression"
    - "First Order Regression: Applications in Real-World Domains"
    - "Stochastic search in inductive concept learning"

Connected through hyperedge HE-7 (induced by source paper "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-sensitive attribute estimation in regression"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

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