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: "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"
  Abstract: In many Genetic Algorithms applications the objective is to find a (near-)optimal solution using a limited amount of computation. Given these requirements it is difficult to find a good balance between exploration and exploitation. Usually such a balance is found by tuning the various parameters (like the selective pressure, population size, the mutation- and crossover rate) of the Genetic Algorithm. As an alternative we propose simultaneous tuning of the selective pressure and the disruptiveness of the recombination operators. Our experiments show that the combination of a proper selective pressure and a highly disruptive recombination operator yields superior performance. The reduction mechanism used in a Steady-State GA has a strong influence on the optimal crossover disruptiveness. Using the worst fitness deletion strategy the building blocks present in the current best individuals are always preserved. This releases the crossover operator from the burden to maintain good building blocks and allows us to tune crossover disruptiveness to improve the search for better individuals.

Connected through hyperedge HE-1 (induced by source paper "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"):
  with members:
    - "On The State of Evolutionary Computation"
    - "Adapting Crossover in a Genetic Algorithm"
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "Crossover or Mutation?"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"

Connected through hyperedge HE-2 (induced by source paper "Genetic algorithms with multi-parent recombination"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Multi-parent Recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"
    - "Solving 3-SAT by GAs Adapting Constraint Weights"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

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