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 "Induction of decision trees using RELIEFF"):
  Abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between them. Greedy search prevents current inductive machine learning algorithms to detect significant dependencies between the attributes. Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that is able to detect dependencies between attributes. We show strong relation between RELIEF's estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms. We propose to use RELIEFF, an extended version of RELIEF, instead of myopic impurity functions. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems. Results show the advantage of the presented approach to inductive learning and open a wide rang of possibilities for using RELIEFF.

Includes members:
  Member "Estimating Attributes: Analysis and Extensions of RELIEF":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries")
  Member "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Discretization of continuous attributes using ReliefF":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Linear Space Induction in First Order Logic with RELIEFF")

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