human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Robot Shaping: Developing Situated Agents through Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "On the Relations Between Search and Evolutionary Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Learning Sequential Decision Rules Using Simulation Models and Competition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "The Role of the Trainer in Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task")
  Member "A Study of the Generalization Capabilities of XCS":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Adding Memory to XCS")
  Member "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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