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: "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
  Abstract: To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966)

Connected through hyperedge HE-1 (induced by source paper "Evolving Graphs and Networks with Edge Encoding: Preliminary Report"):
  with members:
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "An Evolutionary Algorithm that Constructs Recurrent Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "An Evolutionary Approach to Combinatorial Optimization Problems"):
  with members:
    - "Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms"
    - "An Overview of Evolutionary Computation"

Connected through hyperedge HE-3 (induced by source paper "3 Representation Issues in Neighborhood Search and Evolutionary Algorithms"):
  with members:
    - "Evaluating Evolutionary Algorithms"
    - "Hyperplane Ranking in Simple Genetic Algorithms"
    - "Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm"

Connected through hyperedge HE-4 (induced by source paper "Properties of Genetic Representations of Neural Architectures"):
  with members:
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

Connected through hyperedge HE-5 (induced by source paper "Genetic Self-Learning"):
  with members:
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Optimization by Means of Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

Connected through hyperedge HE-6 (induced by source paper "Hierarchical Self-Organization in Genetic Programming"):
  with members:
    - "Causality in Genetic Programming"

Connected through hyperedge HE-7 (induced by source paper "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "Voting for Schemata"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-8 (induced by source paper "The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem"):
  with members:
    - "A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms"

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