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: "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
  Abstract: This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process starts with the random creation of a large population of program trees composed of circuit-constructing functions. Each program tree specifies the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to design. Each fully developed circuit is translated into a netlist, simulated using a modified version of SPICE, and evaluated as to how well it satisfies the user's design requirements. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit, including the circuit's behavior in the time domain, its behavior in the frequency domain, its power consumption, the number of components, cost of components, or surface area occupied by its components. The population of program trees is genetically bred over a series of many generations using genetic programming. Genetic programming is driven by a fitness measure and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring. This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming can evolve the circuit for a difficult-to-design low-pass filter.

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"
    - "Cellular Encoding Applied to Neurocontrol"
    - "Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that"

Connected through hyperedge HE-2 (induced by source paper "Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate"):
  with members:
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"
    - "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"):
  with members:
    - "Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate"
    - "Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming"

Connected through hyperedge HE-4 (induced by source paper "Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming"):
  with members:
    - "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"

Connected through hyperedge HE-5 (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"
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"
    - "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