human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "20 Data Structures and Genetic Programming  two techniques for reducing run time."):
  Abstract: In real world applications, software engineers recognise the use of memory must be organised via data structures and that software using the data must be independant of the data structures' implementation details. They achieve this by using abstract data structures, such as records, files and buffers. We demonstrate that genetic programming can automatically implement simple abstract data structures, considering in detail the task of evolving a list. We show general and reasonably efficient implementations can be automatically generated from simple primitives. A model for maintaining evolved code is demonstrated using the list problem. Much published work on genetic programming (GP) evolves functions without side-effects to learn patterns in test data. In contrast human written programs often make extensive and explicit use of memory. Indeed memory in some form is required for a programming system to be Turing Complete, i.e. for it to be possible to write any (computable) program in that system. However inclusion of memory can make the interactions between parts of programs much more complex and so make it harder to produce programs. Despite this it has been shown GP can automatically create programs which explicitly use memory [Teller 1994]. In both normal and genetic programming considerable benefits have been found in adopting a structured approach. For example [Koza 1994] shows the introduction of evolvable code modules (automatically defined functions, ADFs) can greatly help GP to reach a solution. We suggest that a corresponding structured approach to use of data will similarly have significant advantage to GP. Earlier work has demonstrated that genetic programming can automatically generate simple abstract data structures, namely stacks and queues [Langdon 1995a]. That is, GP can evolve programs that organise memory (accessed via simple read and write primitives) into data structures which can be used by external software without it needing to know how they are implemented. This chapter shows it is possible to evolve a list data structure from basic primitives. [Aho, Hopcroft and Ullman 1987] suggest three different ways to implement a list but these experiments show GP can evolve its own implementation. This requires all the list components to agree on one implementation as they co-evolve together. Section 20.3 describes the GP architecture, including use of Pareto multiple component fitness scoring (20.3.4) and measures aimed at speeding the GP search (20.3.5). The evolved solutions are described in Section 20.4. Section 20.5 presents a candidate model for maintaining evolved software. This is followed by a discussion of what we have learned (20.6) and conclusions that can be drawn (20.7).

Includes members:
  Member "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Evolving Graphs and Networks with Edge Encoding: Preliminary Report")
  Member "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming")
  Member "Context Preserving Crossover in Genetic Programming.":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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