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: "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.

Connected through hyperedge HE-1 (induced by source paper "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"

Connected through hyperedge HE-2 (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-3 (induced by source paper "Improving Tactical Plans with Genetic Algorithms"):
  with members:
    - "The Exploitation of Cooperation in Iterated Prisoner's Dilemma"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Evaluation and Selection of Biases in Machine Learning"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"
    - "EVOLVING ROBOT BEHAVIORS"

Connected through hyperedge HE-4 (induced by source paper "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "EVOLVING ROBOT BEHAVIORS"
    - "ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES"
    - "An Evolutionary Approach to Learning in Robots"

Connected through hyperedge HE-5 (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"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"

Connected through hyperedge HE-6 (induced by source paper "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Decision-Theoretic Case-Based Reasoning"

Connected through hyperedge HE-7 (induced by source paper "AN ENHANCER FOR REACTIVE PLANS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Explanations of Empirically Derived Reactive Plans"

Connected through hyperedge HE-8 (induced by source paper "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Explanations of Empirically Derived Reactive Plans"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"
    - "An Evolutionary Approach to Learning in Robots"

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