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: "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"
  Abstract: An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, an approach to the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans utilizing the stored mental models. In this approach, agents are multipart computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the approach in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. 1. Introduction

Connected through hyperedge HE-1 (induced by source paper "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "Stochastic Random or probabilistic but with some direction. For example the arrival of people at"
    - "The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their"
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"

Connected through hyperedge HE-2 (induced by source paper "Cultural Transmission of Information in Genetic Programming"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "20 Data Structures and Genetic Programming  two techniques for reducing run time."):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Context Preserving Crossover in Genetic Programming."

Connected through hyperedge HE-4 (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."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Analysis of Neurocontrollers Designed by Simulated Evolution"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Properties of Genetic Representations of Neural Architectures"
    - "Issues in Evolutionary Robotics"

Connected through hyperedge HE-6 (induced by source paper "A Comparison of Crossover and Mutation in Genetic Programming"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-7 (induced by source paper "Neural Programming and an Internal Reinforcement Policy"):
  with members:
    - "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"
    - "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"

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