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: "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
  Abstract: The multi-parent scanning crossover, generalizing the traditional uniform crossover, and diagonal crossover, generalizing 1-point (n-point) crossovers, were introduced in [5]. In subsequent publications, see [6, 18, 19], several aspects of multi-parent recombination are discussed. Due to space limitations, however, a full overview of experimental results showing the performance of multi-parent GAs on numerical optimization problems has never been published. This technical report is meant to fill this gap and make results available.

Connected through hyperedge HE-1 (induced by source paper "Multi-parent's niche: n-ary crossovers on NK-landscapes"):
  with members:
    - "Genetic algorithms with multi-parent recombination"
    - "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"

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 "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Generation Gaps Revisited"
    - "Case-Based Probability Factoring in Bayesian Belief Networks"
    - "A Genetic Algorithm for File and Task Placement in a Distributed System"
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Genetic algorithms with multi-parent recombination"
    - "Heuristic for Improved Genetic Bin Packing"
    - "A PARALLEL ISLAND MODEL GENETIC ALGORITHM FOR THE MULTIPROCESSOR SCHEDULING PROBLEM"

Connected through hyperedge HE-4 (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"
    - "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"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

Connected through hyperedge HE-5 (induced by source paper "A Cooperative Coevolutionary Approach to Function Optimization"):
  with members:
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"
    - "Evolving Complex Structures via Co- operative Coevolution"

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