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: "Coevolving Communicative Behavior in a Linear Pursuer-Evader Game"
  Abstract: The pursuer-evader (PE) game is recognized as an important domain in which to study the coevolution of robust adaptive behavior and protean behavior (Miller and Cliff, 1994). Nevertheless, the potential of the game is largely unrealized due to methodological hurdles in coevolutionary simulation raised by PE; versions of the game that have optimal solutions (Isaacs, 1965) are closed-ended, while other formulations are opaque with respect to their solution space, for the lack of a rigorous metric of agent behavior. This inability to characterize behavior, in turn, obfuscates coevolutionary dynamics. We present a new formulation of PE that affords a rigorous measure of agent behavior and system dynamics. The game is moved from the two-dimensional plane to the one-dimensional bit-string; at each time step, the evader generates a bit that the pursuer must simultaneously predict. Because behavior is expressed as a time series, we can employ information theory to provide quantitative analysis of agent activity. Further, this version of PE opens vistas onto the communicative component of pursuit and evasion behavior, providing an open-ended serial communications channel and an open world (via coevolution). Results show that subtle changes to our game determine whether it is open-ended, and profoundly affect the viability of arms-race dynamics.

Connected through hyperedge HE-1 (induced by source paper "An Evolutionary Algorithm that Constructs Recurrent Neural Networks"):
  with members:
    - "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"
    - "The Evolution of Communication Schemes Over Continuous Channels"

Connected through hyperedge HE-2 (induced by source paper "The Evolution of Communication Schemes Over Continuous Channels"):
  with members:
    - "An Evolutionary Algorithm that Constructs Recurrent Neural Networks"

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