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: "ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES"
  Abstract: Autonomous vehicles are likely to require sophisticated software controllers to maintain vehicle performance in the presence of vehicle faults. The test and evaluation of complex software controllers is expected to be a challenging task. The goal of this e ffort is to apply machine learning techniques from the field of arti ficial intelligence to the general problem of evaluating an intelligent controller for an autonomous vehicle. The approach involves subjecting a controller to an adaptively chosen set of fault scenarios within a vehicle simulator, and searching for combinations of faults that produce noteworthy performance by the vehicle controller. The search employs a genetic algorithm. We illustrate the approach by evaluating the performance of a subsumption-based controller for an autonomous vehicle. The preliminary evidence suggests that this approach is an e ffective alternative to manual testing of sophisticated software controllers.

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 "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"):
  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"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"
    - "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"

Connected through hyperedge HE-3 (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"
    - "LEARNING ROBOT BEHAVIORS USING 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"
    - "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