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: "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing"
  Abstract: This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery problems which have been previously tackled only with GP. All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm [8]. We feel it is not intuitively obvious that mutation-based adaptive search can handle program discovery yet, to date, for each GP problem we have tried, SA or SIHC also work.

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 "Hybridized Crossover-Based Search Techniques for Program Discovery"):
  with members:
    - "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"
    - "The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and"

Connected through hyperedge HE-3 (induced by source paper "Investigating the Generality of Automatically Defined Functions"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "The Schema Theorem and Price's Theorem"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

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