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 "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"):
  Abstract: Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may be costly or dangerous. In addition, time constraints might limit the number of experiences during learning in the real world, while in many cases, the simulation model can be made to run faster than real time. Since learning may require experimenting with behaviors that might occasionally produce unacceptable results if applied to the real world, or might require too much time in the real environment, we assume that hypothetical behaviors will be evaluated in a simulation model (the off-line system). As illustrated in Figure 1, the current best behavior can be placed in the real, on-line system, while learning continues in the off-line system [1]. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. The expectation is that behaviors learned in these simulations will be useful in real-world environments. Previous studies have illustrated that knowledge learned under simulation is robust and might be applicable to the real world if the simulation is more general (i.e. has more noise, more varied conditions, etc.) than the real world environment [2]. Where this is not possible, it is important to identify the differences between the simulation and the world and note the effect upon the learning process. The research reported here continues to examine this hypothesis. The next section very briefly explains the learning algorithm (and gives pointers to where more extensive documentation can be found). After that, the actual robot is described. Then we describe the simulation of the robot. The task _______________ actual robot.

Includes members:
  Member "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological")
  Member "Learning Sequential Decision Rules Using Simulation Models and Competition":
    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 "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation":
    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 "Improving Tactical Plans with Genetic Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "EVOLVING ROBOT BEHAVIORS")
  Member "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")
  Member "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")
  Member "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")
  Member "AN ENHANCER FOR REACTIVE PLANS":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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