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: "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"
  Abstract: Technical Report: CSRP-96-14 August 1996 Abstract Genetic Programming is a method of program discovery consisting of a special kind of genetic algorithm capable of operating on parse trees representing programs and an interpreter which can run the programs being optimised. This paper describes Parallel Distributed Genetic Programming (PDGP), a new form of genetic programming which is suitable for the development of parallel programs in which symbolic and neural processing elements can be combined a in free and natural way. PDGP is based on a graph-like representation for parallel programs which is manipulated by crossover and mutation operators which guarantee the syntactic correctness of the offspring. The paper describes these operators and reports some results obtained with the exclusive-or problem.

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"
    - "Cellular Encoding Applied to Neurocontrol"
    - "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 "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evolving Visual Routines"
    - "Program Optimization for Faster Genetic Programming"
    - "Induction and Recapitulation of Deep Musical Structure"
    - "Cellular Encoding for Interactive Evolutionary Robotics"

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:
    - "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"
    - "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 "Some Steps Towards a Form of Parallel Distributed Genetic Programming"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Neural Programming and an Internal Reinforcement Policy"):
  with members:
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"
    - "How Fitness Structure Affects Subsolution Acquisition in 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