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: "Dynamic Hill Climbing: Overcoming the limita- tions of optimization techniques"
  Abstract: This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic and hill climbing algorithms, dynamic hill climbing has the ability to dynamically change its coordinate frame during the course of an optimization. Furthermore, the algorithm moves from a coarse-grained search to a fine-grained search of the function space by changing its mutation rate and uses a diversity-based distance metric to ensure that it searches new regions of the space. Dynamic hill climbing is empirically compared to a traditional genetic algorithm using De Jong's well-known five function test suite [4] and is shown to vastly surpass the performance of the genetic algorithm, often finding better solutions using only 1% as many function evaluations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms"):
  with members:
    - "A Survey of Evolution Strategies"

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