human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "An adaptation of Relief for attribute estimation in regression"):
  Abstract: Heuristic measures for estimating the quality of attributes mostly assume the independence of attributes so in domains with strong dependencies between attributes their performance is poor. Relief and its extension ReliefF are capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view. We present the analysis of Reli-efF which lead us to its adaptation to regression (continuous class) problems. The experiments on artificial and real-world data sets show that Re-gressional ReliefF correctly estimates the quality of attributes in various conditions, and can be used for non-myopic learning of the regression trees. Regressional ReliefF and ReliefF provide a unified view on estimating the attribute quality in regression and classification.

Includes members:
  Member "Flexible Metric Nearest Neighbor Classiflcation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning")
  Member "Employing Linear Regression in Regression Tree Leaves":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "First Order Regression")
  Member "Estimating Attributes: Analysis and Extensions of RELIEF":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries")
  Member "Context-Sensitive Feature Selection for Lazy Learners":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts")
  Member "Overcoming the myopia of inductive learning algorithms with RELIEFF":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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