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: "Vector Quantizer Design Using Genetic Algorithms"
  Abstract: A Genetic Algorithmic (GA) approach to vector quantizer design that combines the conventional Generalized Lloyd Algorithm (GLA) [6] is presented. We refer to this hybrid as the Genetic Generalized Lloyd Algorithm (GGLA). It works briefly as follows: A finite number of codebooks, called chromosomes, are selected. Each codebook undergoes iterative cycles of reproduction. We perform experiments with various alternative design choices using Gaussian-Markov processes, speech, and image as source data and signal-to-noise ratio (SNR) as the performance measure. In most cases, the GGLA showed performance improvements with respect to the GLA. We also compare our results with the Zador-Gersho formula [2, 9].

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 "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "HGA: A Hardware-Based Genetic Algorithm"
    - "A Comparative Study of Genetic Search"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "How good are genetic algorithms at finding large cliques: an experimental study"
    - "An Evolutionary Approach to Vector Quantizer Design"
    - "Using Genetic Algorithms for Supervised Concept Learning"
    - "A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms"

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