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: "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"
  Abstract: NK-landscapes offer the ability to assess the performance of evolutionary algorithms on problems with different degrees of epistasis. In this paper, we study the performance of six algorithms in NK-landscapes with low and high dimension while keeping the amount of epistatic interactions constant. The results show that compared to genetic local search algorithms, the performance of standard genetic algorithms employing crossover or mutation significantly decreases with increasing problem size. Furthermore, with increasing K, crossover based algorithms are in both cases outperformed by mutation based algorithms. However, the relative performance differences between the algorithms grow significantly with the dimension of the search space, indicating that it is important to consider high-dimensional landscapes for evaluating the performance of evolutionary algorithms.

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"
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"

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 "A Genetic Local Search Approach to the Quadratic Assignment Problem"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Using Problem Generators to Explore the Effects of Epistasis"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "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