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: "Real-time Interactive Neuro-evolution"
  Abstract: In standard neuro-evolution, a population of networks is evolved in the task, and the network that best solves the task is found. This network is then fixed and used to solve future instances of the problem. Networks evolved in this way do not handle real-time interaction very well. It is hard to evolve a solution ahead of time that can cope effectively with all the possible environments that might arise in the future and with all the possible ways someone may interact with it. This paper proposes evolving feedforward neural networks online to create agents that improve their performance through real-time interaction. This approach is demonstrated in a game world where neural-network-controlled individuals play against humans. Through evolution, these individuals learn to react to varying opponents while appropriately taking into account conflicting goals. After initial evaluation offline, the population is allowed to evolve online, and its performance improves considerably. The population not only adapts to novel situations brought about by changing strategies in the opponent and the game layout, but it also improves its performance in situations that it has already seen in offline training. This paper will describe an implementation of online evolution and shows that it is a practical method that exceeds the performance of offline evolution alone.

Connected through hyperedge HE-1 (induced by source paper "Evolving Neural Networks to Play Go"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Incremental Evolution of Complex General Behavior"):
  with members:
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"

Connected through hyperedge HE-3 (induced by source paper "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"):
  with members:
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-4 (induced by source paper "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"

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