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: "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"
  Abstract: This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced by a controller that pushes the cart to the left or the right. In some cases velocity information about the pole and cart is provided as an input; in other cases the network must learn to balance a single pole without velocity information. A careful study of the behavior of these systems suggests that it is possible to balance a single pole with velocity information as an input and without learning to compute the velocity. A new fitness function is introduced that forces the neural network to compute the velocity. By using this new fitness function and tuning the syntactic constraints used with cellular encoding, we achieve a tenfold speedup over our previous study and solve a more difficult problem: balancing two poles when no information about the velocity is provided as input.

Connected through hyperedge HE-1 (induced by source paper "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"

Connected through hyperedge HE-2 (induced by source paper "Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"):
  with members:
    - "Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate"
    - "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"
    - "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"
    - "Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming"

Connected through hyperedge HE-4 (induced by source paper "Cellular Encoding Applied to Neurocontrol"):
  with members:
    - "Culling Teaching -1 Culling and Teaching in Neuro-evolution"

Connected through hyperedge HE-5 (induced by source paper "The Role of Development in Genetic Algorithms"):
  with members:
    - "Evolution in Time and Space The Parallel Genetic Algorithm"

Connected through hyperedge HE-6 (induced by source paper "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"):
  with members:
    - "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
    - "Neural Programming and an Internal Reinforcement Policy"
    - "Some Steps Towards a Form of Parallel Distributed 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