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: "3 Representation Issues in Neighborhood Search and Evolutionary Algorithms"
  Abstract: Evolutionary Algorithms are often presented as general purpose search methods. Yet, we also know that no search method is better than another over all possible problems and that in fact there is often a good deal of problem specific information involved in the choice of problem representation and search operators. In this paper we explore some very general properties of representations as they relate to neighborhood search methods. In particular, we looked at the expected number of local optima under a neighborhood search operator when averaged overall possible representations. The number of local optima under a neighborhood search operator for standard Binary and standard binary reflected Gray codes is developed and explored as one measure of problem complexity. We also relate number of local optima to another metric, , designed to provide one measure of complexity with respect to a simple genetic algorithm. Choosing a good representation is a vital component of solving any search problem. However, choosing a good representation for a problem is as difficult as choosing a good search algorithm for a problem. Wolpert and Macready's (1995) No Free Lunch (NFL) theorem proves that no search algorithm is better than any other over all possible discrete functions. Radcliffe and Surry (1995) extend these notions to also cover the idea that all representations are equivalent when their behavior is considered on average over all possible functions. To understand these results, we first outline some of the simple assumptions behind this theorem. First, assume the optimization problem is discrete; this describes all combinatorial optimization problems-and really all optimization problems being solved on computers since computers have finite precision. Second, we ignore the fact that we can resample points in the space. The "No Free Lunch" result can be stated as follows:

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 "Hyperplane Ranking in Simple Genetic Algorithms"):
  with members:
    - "Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm"):
  with members:
    - "Hyperplane Ranking in Simple Genetic Algorithms"
    - "Walsh Functions and Predicting Problem Complexity"

Connected through hyperedge HE-4 (induced by source paper "Evaluating Evolutionary Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Island Model Genetic Algorithms and Linearly Separable Problems"
    - "Staged Hybrid Genetic Search for Seismic Data Imaging"
    - "A Survey of Evolution Strategies"

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