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: "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"
  Abstract: Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that defines this solution, each choice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path from the root of that tree to a leaf. This paper describes how the simulated evolution of a population of such non-deterministic incremental algorithms offers a new approach for the exploration of a state space, compared to other techniques like Genetic Algorithms (GA), Evolutionary Strategies (ES) or Hill Climbing. In particular, the efficiency of this method, implemented as the Evolving Non-Determinism (END) model, is presented for the sorting network problem, a reference problem that has challenged computer science. Then, we shall show that the END model remedies some drawbacks of these optimization techniques and even outperforms them for this problem. Indeed, some 16-input sorting networks as good as the best known have been built from scratch, and even a 25-year-old result for the 13-input problem has been improved by one comparator.

Connected through hyperedge HE-1 (induced by source paper "Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays Convergent Design, L.L.C."):
  with members:
    (none)

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:
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
    - "Computation. Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"):
  with members:
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"

Connected through hyperedge HE-4 (induced by source paper "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"):
  with members:
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"

Connected through hyperedge HE-5 (induced by source paper "A Stochastic Search Approach to Grammar Induction"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"
    - "A Survey of Evolution Strategies"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

Connected through hyperedge HE-6 (induced by source paper "Dynamic Parameter Encoding for Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "Representation and Evolution of Neural Networks"
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"

Connected through hyperedge HE-7 (induced by source paper "A Survey of Evolution Strategies"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "A Stochastic Search Approach to Grammar Induction"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Evaluating Evolutionary Algorithms"
    - "Optimal Mutation Rates in Genetic Search"
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

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