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: "Toward Simulated Evolution of Machine-Language Iteration"
  Abstract: We use a simulated evolution search (genetic programming) for the automatic synthesis of small iterative machine-language programs. For an integer register machine with an addition instruction as its sole arithmetic operator, we show that genetic programming can produce exact and general multiplication routines by synthesizing the necessary iterative control structures from primitive machine-language instructions. Our program representation is a virtual register machine that admits arbitrary control flow. Our evolution strategy furthermore does not artificially restrict the synthesis of any control structure; we only place an upper bound on program evaluation time. A program's fitness is the distance between the output produced by a test case and the desired output (multiplication). The test cases exhaustively cover multiplication over a finite subset of the natural numbers (N 10 ); yet the derived solutions constitute general multiplication for the positive integers. For this problem, simulated evolution with a two-point crossover operator examines significantly fewer individuals in finding a solution than random search. Introduction of a small rate of mutation fur ther increases the number of solutions.

Connected through hyperedge HE-1 (induced by source paper "Learning Recursive Sequences via Evolution of Machine-Language Programs"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "Fitness Landscapes and Difficulty in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming and Redundancy"
    - "Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets"
    - "The Schema Theorem and Price's Theorem"
    - "A Simulation of Adaptive Agents in a Hostile Environment"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Complexity Compression and Evolution"

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