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: "Automatic Modularization by Speciation"
  Abstract: Real-world problems are often too difficult to be solved by a single monolithic system. There are many examples of natural and artificial systems which show that a modular approach can reduce the total complexity of the system while solving a difficult problem satisfactorily. The success of modular artificial neural networks in speech and image processing is a typical example. However, designing a modular system is a difficult task. It relies heavily on human experts and prior knowledge about the problem. There is no systematic and automatic way to form a modular system for a problem. This paper proposes a novel evolutionary learning approach to designing a modular system automatically, without human intervention. Our starting point is speciation, using a technique based on fitness sharing. While speciation in genetic algorithms is not new, no effort has been made towards using a speciated population as a complete modular system. We harness the specialized expertise in the species of an entire population, rather than a single individual, by introducing a gating algorithm. We demonstrate our approach to automatic modularization by improving co-evolutionary game learning. Following earlier researchers, we learn to play iterated prisoner's dilemma. We review some problems of earlier co-evolutionary learning, and explain their poor generalization ability and sudden mass extinctions. The generalization ability of our approach is significantly better than past efforts. Using the specialized expertise of the entire speciated population though a gating algorithm, instead of the best individual, is the main contributor to this improvement.

Connected through hyperedge HE-1 (induced by source paper "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Evolving Complex Structures via Co- operative Coevolution"
    - "Self-Nonself Discrimination in a Computer"

Connected through hyperedge HE-2 (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"
    - "Knowledge-Based Genetic Learning"
    - "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"
    - "Evolving Complex Structures via Co- operative Coevolution"

Connected through hyperedge HE-3 (induced by source paper "New Methods for Competitive Coevolution"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "17 Massively Parallel Genetic Programming"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"
    - "Dynamics of Co-evolutionary Learning"

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