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: "Grounding Robotic Control with Genetic Neural Networks"
  Abstract: Technical Report AI94-223 May 1994 Abstract An important but often neglected problem in the field of Artificial Intelligence is that of grounding systems in their environment such that the representations they manipulate have inherent meaning for the system. Since humans rely so heavily on semantics, it seems likely that the grounding is crucial to the development of truly intelligent behavior. This study investigates the use of simulated robotic agents with neural network processors as part of a method to ensure grounding. Both the topology and weights of the neural networks are optimized through genetic algorithms. Although such comprehensive optimization is difficult, the empirical evidence gathered here shows that the method is not only tractable but quite fruitful. In the experiments, the agents evolved a wall-following control strategy and were able to transfer it to novel environments. Their behavior suggests that they were also learning to build cognitive maps.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evolving Graphs and Networks with Edge Encoding: Preliminary Report"

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