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: "Cellular Encoding Applied to Neurocontrol"
  Abstract: Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analyzed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve.

Connected through hyperedge HE-1 (induced by source paper "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"):
  with members:
    - "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"
    - "The Role of Development in Genetic Algorithms"
    - "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
    - "Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that"

Connected through hyperedge HE-2 (induced by source paper "Culling Teaching -1 Culling and Teaching in Neuro-evolution"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Complexity Compression and Evolution"
    - "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"

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