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 Self-Learning"
  Abstract: Evolutionary Algorithms are direct random search algorithms which imitate the principles of natural evolution as a method to solve adaptation (learning) tasks in general. As such they have several features in common which can be observed on the genetic and phenotypic level of living species. In this paper the algorithms' capability of adaptation or learning in a wider sense is demonstrated, and it is focused on Genetic Algorithms to illustrate the learning process on the population level (first level learning), and on Evolution Strategies to demonstrate the learning process on the meta-level of strategy parameters (second level learning).

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 "Optimization by Means of Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"

Connected through hyperedge HE-3 (induced by source paper "Extended Selection Mechanisms in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Optimization by Means of Genetic Algorithms"
    - "A Survey of Evolution Strategies"
    - "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"
    - "Pruning backpropagation neural networks using modern stochastic optimization techniques"

Connected through hyperedge HE-4 (induced by source paper "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "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