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: "learning easier tasks. More work is necessary in order to determine more precisely the relationship"
  Abstract: We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty.

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"
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"

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"
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"

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."
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"
    - "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