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: "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
  Abstract: An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using Genetic Programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations . An illustrative problem of 'gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans.

Connected through hyperedge HE-1 (induced by source paper "The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Evolution of Iteration in Genetic Programming D a v d A The solution to many"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"

Connected through hyperedge HE-3 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Evolving Teamwork and Coordination with Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"
    - "Neural Programming and an Internal Reinforcement Policy"

Connected through hyperedge HE-4 (induced by source paper "Stochastic Random or probabilistic but with some direction. For example the arrival of people at"):
  with members:
    - "Learning Monitoring Strategies: A Difficult Genetic Programming Application"

Connected through hyperedge HE-5 (induced by source paper "learning easier tasks. More work is necessary in order to determine more precisely the relationship"):
  with members:
    - "Adaptation in constant utility non-stationary environments"
    - "Adapting the Evaluation Space to Improve Global 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