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: "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
  Abstract: Anaplastic thyroid carcinoma is a rare but very aggressive tumor. Many factors that might influence the survival of patients have been suggested. The aim of our study was to determine which of the factors, known at the time of admission to the hospital, might predict survival of patients with anaplastic thyroid carcinoma. Our aim was also to assess the relative importance of the factors and to identify potentially useful decision and regression trees generated by machine learning algorithms. Our study included 126 patients (90 females and 36 males; mean age was 66.7 years) with anaplastic thyroid carcinoma treated at the Institute of Oncology Ljubljana from 1972 to 1992. Patients were classified into categories according to 11 attributes: sex, age, history, physical findings, extent of disease on admission, and tumor morphology. In this paper we compare the machine learning approach with the previous statistical evaluations on the problem (uni-variate and multivariate analysis) and show that it can provide more thorough analysis and improve understanding of the data.

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"
    - "An adaptation of Relief for attribute estimation in regression"
    - "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"
    - "An adaptation of Relief for attribute estimation in regression"
    - "First Order Regression"
    - "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 "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"
    - "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-4 (induced by source paper "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Learning Symbolic Rules Using Artificial Neural Networks"

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"
    - "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"

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