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: "Rule Induction with CN2: Some Recent Improvements"
  Abstract: The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made.

Connected through hyperedge HE-1 (induced by source paper "Multi-class problems and discretization in ICL Extended abstract"):
  with members:
    - "Inductive Constraint Logic"
    - "Inductive Constraint Logic and the Mutagenesis Problem"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-2 (induced by source paper "Incremental Reduced Error Pruning"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "An investigation of noise-tolerant relational concept learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Induction of decision trees using RELIEFF"

Connected through hyperedge HE-4 (induced by source paper "Stochastically Guided Disjunctive Version Space Learning"):
  with members:
    - "Error Reduction through Learning Multiple Descriptions"
    - "Learning Decision Lists Using Homogeneous Rules"
    - "Classifiers: A Theoretical and Empirical Study"

Connected through hyperedge HE-5 (induced by source paper "Using Qualitative Models to Guide Inductive Learning"):
  with members:
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"
    - "A Divide-and-Conquer Approach to Learning from Prior Knowledge"

Connected through hyperedge HE-6 (induced by source paper "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"):
  with members:
    - "An intelligent search method using Inductive Logic Programming"
    - "Inductive Constraint Logic"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-7 (induced by source paper "Fossil: A Robust Relational Learner"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Predicting Ordinal Classes in ILP"
    - "An investigation of noise-tolerant relational concept learning algorithms"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Top-Down Pruning in Relational Learning"

Connected through hyperedge HE-8 (induced by source paper "Case-Based Sonogram Classification"):
  with members:
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Concept Learning and Flexible Weighting"

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