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: "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"
  Abstract: It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system and the background genetic algorithm (GA), rule chains instability, default hierarchies instability, are only a few. ALECSYS is a parallel version of a standard learning classifier system (CS), and as such suffers of these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec, a new genetic operator used to specialize potentially useful classifiers. Energy, a quantity introduced to measure global convergence in order to apply the genetic algorithm only when the system is close to a steady state. Dynamical adjustment of the classifiers set cardinality, in order to speed up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source.

Connected through hyperedge HE-1 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Exploitation of Cooperation in Iterated Prisoner's Dilemma"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-2 (induced by source paper "On the Relations Between Search and Evolutionary Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-3 (induced by source paper "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"

Connected through hyperedge HE-4 (induced by source paper "The Role of the Trainer in Reinforcement Learning"):
  with members:
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"

Connected through hyperedge HE-5 (induced by source paper "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "The Role of the Trainer in Reinforcement Learning"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"
    - "An Evolutionary Approach to Learning in Robots"

Connected through hyperedge HE-6 (induced by source paper "Robot Shaping: Developing Situated Agents through Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "The Role of the Trainer in Reinforcement Learning"

Connected through hyperedge HE-7 (induced by source paper "A Study of the Generalization Capabilities of XCS"):
  with members:
    - "Model of the Environment to Avoid Local Learning"
    - "Adding Memory to XCS"
    - "Environments with Classifier Systems (Experiments on Adding Memory to XCS)"

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