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 of Iteration in Genetic Programming D a v d A The solution to many"
  Abstract: This paper introduces the new operation of restricted iteration creation that automatically Genetic programming extends Holland's genetic algorithm to the task of automatic programming. Early work on genetic programming demonstrated that it is possible to evolve a sequence of work-performing steps in a single result-producing branch (that is, a one-part "main" program). The book Genetic Programming: On the Programming of Computers by Means of Natural Selection (Koza 1992) describes an extension of Holland's genetic algorithm in which the genetic population consists of computer programs (that is, compositions of primitive functions and terminals). See also Koza and Rice (1992). In the most basic form of genetic programming (where only a single result-producing branch is evolved), genetic programming demonstrated the capability to discover a sequence (as to both its length and its content) of work-performing steps that is sufficient to produce a satisfactory solution to several problems, including many problems that have been used over the years as benchmarks in machine learning and artificial intelligence. Before applying genetic programming to a problem, the user must perform five major preparatory steps, namely identifying the terminals (inputs) of the to-be-evolved programs, identifying the primitive functions (operations) contained in the to-be-evolved programs, creating the fitness measure for evaluating how well a given program does at solving the problem at hand, choosing certain control parameters (notably population size and number of generations to be run), and determining the termination criterion and method of result designation (typically the best-so-far individual from the populations produced during the run). creates a restricted iteration-performing

Connected through hyperedge HE-1 (induced by source paper "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"):
  with members:
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"
    - "Stochastic Random or probabilistic but with some direction. For example the arrival of people at"
    - "The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their"
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"

Connected through hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-3 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Evolving Teamwork and Coordination with Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"
    - "Neural Programming and an Internal Reinforcement Policy"

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