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: "Relating Relational Learning Algorithms"
  Abstract: Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research.

Connected through hyperedge HE-1 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  with members:
    - "Inductive Logic Programming"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Observation and Generalisation in a Simulated Robot World"
    - "Learning First-Order Acyclic Horn Programs from Entailment"
    - "The Origins of Inductive Logic Programming: A Prehistoric Tale"

Connected through hyperedge HE-2 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-3 (induced by source paper "An Improved Algorithm for Incremental Induction of Decision Trees"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Theory Revision in Fault Hierarchies"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Learning Classification Trees"
    - "Geometric Comparison of Classifications and Rule Sets*"
    - "What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog"

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: Case_Based