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: "Context-sensitive attribute estimation in regression"
  Abstract: One of key issues in both discrete and continuous class prediction and in machine learning in general seems to be the problem of estimating the quality of attributes. Heuristic measures mostly assume independence of attributes so their use is non-optimal in domains with strong dependencies between attributes. For the same reason they are also mostly unable to recognize context dependent features. Relief and its extension Re-liefF are statistical methods 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 and recognize contextual attributes. After the analysis of ReliefF we have extended it to continuous class problems. Regressional ReliefF (RReliefF) and ReliefF provide a unified view on estimating attribute quality. The experiments show that RReliefF correctly estimates the quality of attributes, recognizes the contextual attributes and can be used for non myopic learning of the regression trees.

Connected through hyperedge HE-1 (induced by source paper "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"):
  with members:
    - "Induction of Selective Bayesian Classifiers"
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Learning One More Thing"
    - "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"

Connected through hyperedge HE-2 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "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 "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "Structural Regression Trees"
    - "An adaptation of Relief for attribute estimation in regression"
    - "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"
    - "The Bayesian Approach to Tree-Structured Regression"

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"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "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"
    - "An adaptation of Relief for attribute estimation in regression"
    - "The Management of Context-Sensitive Features: A Review of Strategies"

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"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Overcoming the myopia of inductive learning algorithms with 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