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: "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.

Connected through hyperedge HE-1 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "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-2 (induced by source paper "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "Structural Regression Trees"
    - "First Order Regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Producing More Comprehensible Models While Retaining Their Performance"
    - "Context-sensitive attribute estimation in regression"
    - "The Bayesian Approach to Tree-Structured Regression"

Connected through hyperedge HE-3 (induced by source paper "Flexible Metric Nearest Neighbor Classiflcation"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."
    - "Virtual Seens and the Frequently Used Dataset"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "DART/HYESS Users Guide  recursive covering approach to local learning"
    - "Selection of Relevant Features and Examples in Machine Learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "Linear Space Induction in First Order Logic with RELIEFF"

Connected through hyperedge HE-5 (induced by source paper "Context-Sensitive Feature Selection for Lazy Learners"):
  with members:
    - "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts"
    - "Learning to Refine Case Libraries:"
    - "Refining Conversational Case Libraries"
    - "Context-sensitive attribute estimation in regression"
    - "The Management of Context-Sensitive Features: A Review of Strategies"

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