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: "Culling Teaching -1 Culling and Teaching in Neuro-evolution"
  Abstract: The evolving population of neural nets contains information not only in terms of genes, but also in the collection of behaviors of the population members. Such information can be thought of as a kind of culture of the population. Two ways of exploiting that culture are explored in this paper: (1) Culling overlarge litters: Generate a large number of offspring with different crossovers, quickly evaluate them by comparing their performance to the population, and throw away those that appear poor. (2) Teaching: Use backpropagation to train offspring toward the performance of the population. Both techniques result in faster, more effective neuro-evolution, and they can be effectively combined, as is demonstrated on the inverted pendulum problem. Additional methods of cultural exploitation are possible and will be studied in future work. These results suggest that cultural exploitation is a powerful idea that allows leveraging several aspects of the genetic algorithm.

Connected through hyperedge HE-1 (induced by source paper "Cellular Encoding Applied to Neurocontrol"):
  with members:
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Complexity Compression and Evolution"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Signal Path Oriented Approach for Generation of Dynamic Process Models"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "A Survey of Intron Research in Genetics"

Connected through hyperedge HE-3 (induced by source paper "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"):
  with members:
    - "Landscapes, Learning Costs and Genetic Assimilation."
    - "On the relationship between distributed group-behaviour and the behavioural complexity of individuals [Wilson Sober 1994])"
    - "Artificial Life as Theoretical Biology: How to do real science with computer simulation"

Connected through hyperedge HE-4 (induced by source paper "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time"
    - "Q-Learning with Hidden-Unit Restarting"

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