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: "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"
  Abstract: The design (synthesis) of analog electrical circuits starts with a high-level statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a high-level statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification circuit, an amplifier, a computational circuit, a time-optimal controller circuit, a temperaturesensing circuit, and a voltage reference circuit. The problemspecific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuit's desired behavior as a measurable mathematical quantity. The eight genetically evolved circuits constitute an instance of an evolutionary computation technique producing results on a task that is usually thought of as requiring human intelligence. The fact that a single uniform approach yielded a satisfactory design for each of the eight circuits as well as the fact that a satisfactory design was created on the first or second run of each problem are evidence for the general applicability of genetic programming for solving the problem of automatic synthesis of analog electrical circuits.

Connected through hyperedge HE-1 (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"
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"

Connected through hyperedge HE-2 (induced by source paper "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"):
  with members:
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"
    - "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"
    - "Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming"):
  with members:
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"

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