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: "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"
  Abstract: Traditional evolutionary optimization algorithms assume a static evaluation function, according to which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker [6] trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as when they are easier than the target function.

Connected through hyperedge HE-1 (induced by source paper "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"
    - "Stochastic Random or probabilistic but with some direction. For example the arrival of people at"
    - "The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their"
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"

Connected through hyperedge HE-2 (induced by source paper "Adapting the Evaluation Space to Improve Global Learning"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"

Connected through hyperedge HE-3 (induced by source paper "Adaptation in constant utility non-stationary environments"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"
    - "Generalization and scaling in reinforcement learning"

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