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: "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
  Abstract: Genetic algorithms (GAs) play a major role in many artificial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types of fitness landscapes that lead to successful GA performance. In this paper we propose a strategy for addressing these issues. Our strategy consists of defining a set of features of fitness landscapes that are particularly relevant to the GA, and experimentally studying how various configurations of these features affect the GA's performance along a number of dimensions. In this paper we informally describe an initial set of proposed feature classes, describe in detail one such class ("Royal Road" functions), and present some initial experimental results concerning the role of crossover and "building blocks" on landscapes constructed from features of this class.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."):
  with members:
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"

Connected through hyperedge HE-3 (induced by source paper "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-4 (induced by source paper "A comparison of the fixed and floating building block representation in the genetic algorithm"):
  with members:
    - "Empirical studies of the genetic algorithm with non-coding segments"
    - "Duplication of Coding Segments in Genetic Programming"
    - "A Survey of Intron Research in Genetics"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."

Connected through hyperedge HE-5 (induced by source paper "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"
    - "Self-Nonself Discrimination in a Computer"

Connected through hyperedge HE-6 (induced by source paper "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Schema Theorem and Price's Theorem"
    - "When Will a Genetic Algorithm Outperform Hill Climbing?"

Connected through hyperedge HE-7 (induced by source paper "The Impact of External Dependency in Genetic Programming Primitives"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"

Connected through hyperedge HE-8 (induced by source paper "When Will a Genetic Algorithm Outperform Hill Climbing?"):
  with members:
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"

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