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: "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
  Abstract: In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system.

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 "A genetic prototype learner"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "PLEASE: A prototype learning system using genetic algorithms"

Connected through hyperedge HE-3 (induced by source paper "Using Genetic Algorithms for Supervised Concept Learning"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "Knowledge-Based Genetic Learning"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Adaptive Strategy Selection for Concept Learning"
    - "A Survey of Evolution Strategies"
    - "Is Consistency Harmful?"

Connected through hyperedge HE-4 (induced by source paper "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Generation Gaps Revisited"
    - "Case-Based Probability Factoring in Bayesian Belief Networks"
    - "A Genetic Algorithm for File and Task Placement in a Distributed System"
    - "Genetic algorithms with multi-parent recombination"
    - "Heuristic for Improved Genetic Bin Packing"
    - "A PARALLEL ISLAND MODEL GENETIC ALGORITHM FOR THE MULTIPROCESSOR SCHEDULING PROBLEM"
    - "A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM"

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