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: "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"
  Abstract: This paper describes a hybrid methodology that integrates genetic algorithms and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A genetic algorithm (GA) is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision-tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.

Connected through hyperedge HE-1 (induced by source paper "Evolving Visual Routines"):
  with members:
    - "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"
    - "Evolving Visually Guided Robots"
    - "Generality versus Size in Genetic Programming"
    - "Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The"

Connected through hyperedge HE-2 (induced by source paper "Data Analyses Using Simulated Breeding and Inductive Learning Methods"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Irrelevant Features and the Subset Selection Problem"
    - "Using Genetic Algorithms for Supervised Concept Learning"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-3 (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"
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Predicting Ordinal Classes in ILP"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"

Connected through hyperedge HE-4 (induced by source paper "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"

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: Genetic_Algorithms