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: "Knowledge-Based Genetic Learning"
  Abstract: Genetic algorithms have been proven to be a powerful tool within the area of machine learning. However, there are some classes of problems where they seem to be scarcely applicable, e.g. when the solution to a given problem consists of several parts that influence each other. In that case the classic genetic operators cross-over and mutation do not work very well thus preventing a good performance. This paper describes an approach to overcome this problem by using high-level genetic operators and integrating task specific but domain independent knowledge to guide the use of these operators. The advantages of this approach are shown for learning a rule base to adapt the parameters of an image processing operator path within the SOLUTION system.

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 "Using Genetic Algorithms for Supervised Concept Learning"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Adaptive Strategy Selection for Concept Learning"
    - "A Survey of Evolution Strategies"
    - "Is Consistency Harmful?"

Connected through hyperedge HE-3 (induced by source paper "A Coevolutionary Approach to Learning Sequential Decision Rules"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"

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