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: "Estimating Attributes: Analysis and Extensions of RELIEF"
  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 among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem.

Connected through hyperedge HE-1 (induced by source paper "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Induction of decision trees using RELIEFF"

Connected through hyperedge HE-2 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF"):
  with members:
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Context-sensitive attribute estimation in regression"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-3 (induced by source paper "Prognosing the femoral neck fracture recovery with machine learning"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Naive Bayesian classifier within ILP-R"):
  with members:
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"

Connected through hyperedge HE-5 (induced by source paper "On Biases in Estimating Multi-Valued Attributes"):
  with members:
    - "Discovering Compressive Partial Determinations in Mixed Numerical and Symbolic Domains"

Connected through hyperedge HE-6 (induced by source paper "Context-sensitive attribute estimation in regression"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "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-7 (induced by source paper "Feature Subset Selection as Search with Probabilistic Estimates"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"
    - "Automatic Parameter Selection by Minimizing Estimated Error"

Connected through hyperedge HE-8 (induced by source paper "Induction of decision trees using RELIEFF"):
  with members:
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"
    - "Discretization of continuous attributes using 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