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: "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
  Abstract: This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- and misclassification costs. It does not present any new algorithms but shows how this framework can be used to configure greedy algorithms for constructing such trees or rules. The framework covers a number of existing algorithms. Moreover, the framework can also be used to define algorithm configurations with new functionalities, as expressed in their evaluation functions.

Connected through hyperedge HE-1 (induced by source paper "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"):
  with members:
    - "Learning Approximate Control Rules Of High Utility"
    - "Learning with Rare Cases and Small Disjuncts"
    - "The Problem with Noise and Small Disjuncts"

Connected through hyperedge HE-2 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Learning Active Classifiers"
    - "Predicting Ordinal Classes in ILP"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"

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