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: "Overcoming the myopia of inductive learning algorithms with RELIEFF"
  Abstract: Current inductive machine learning algorithms typically use greedy search with limited looka-head. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELI-EFF, an extension of RELIEF developed by Kira and Rendell [10], [11], for heuristic guidance of inductive learning algorithms. 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 and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning.

Connected through hyperedge HE-1 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming"):
  with members:
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-2 (induced by source paper "Context-sensitive attribute estimation in regression"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "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"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"

Connected through hyperedge HE-3 (induced by source paper "Linear Space Induction in First Order Logic with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Discretization of continuous attributes using ReliefF"

Connected through hyperedge HE-4 (induced by source paper "An adaptation of Relief for attribute estimation in regression"):
  with members:
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Employing Linear Regression in Regression Tree Leaves"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-Sensitive Feature Selection for Lazy Learners"

Connected through hyperedge HE-5 (induced by source paper "Estimating Attributes: Analysis and Extensions of RELIEF"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "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"
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Naive Bayesian classifier within ILP-R"

Connected through hyperedge HE-6 (induced by source paper "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "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,"

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