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: "Genetic algorithms with multi-parent recombination"
  Abstract: In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point crossover or uniform crossover, and is applicable to an arbitrary number (two or more) of parents. We performed extensive tests for optimizing numerical functions, the TSP and graph coloring to observe the effect of different numbers of parents. The experiments show that 2-parent recombination is outperformed when using more parents on the classical DeJong functions. For the other problems the results are not conclusive, in some cases 2 parents are optimal, while in some others more parents are better.

Connected through hyperedge HE-1 (induced by source paper "Multi-parent's niche: n-ary crossovers on NK-landscapes"):
  with members:
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"

Connected through hyperedge HE-2 (induced by source paper "Solving 3-SAT by GAs Adapting Constraint Weights"):
  with members:
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"

Connected through hyperedge HE-3 (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-4 (induced by source paper "Multi-parent Recombination"):
  with members:
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Evolutionary Programming and Evolution Strategies: Similarities and Differences"
    - "Adapting Crossover in Evolutionary Algorithms"

Connected through hyperedge HE-5 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Multi-parent Recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"

Connected through hyperedge HE-6 (induced by source paper "Graph Coloring with Adaptive Evolutionary Algorithms"):
  with members:
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1"
    - "Solving 3-SAT by GAs Adapting Constraint Weights"

Connected through hyperedge HE-7 (induced by source paper "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"):
  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"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

Connected through hyperedge HE-8 (induced by source paper "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"):
  with members:
    - "An Analysis of the Interacting Roles of Population Size and Crossover in 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