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: "Evolving Graphs and Networks with Edge Encoding: Preliminary Report"
  Abstract: We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental investigation of the relative merits of these encoding schemes is currently in progress.

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 "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."
    - "Coevolving Communicative Behavior in a Linear Pursuer-Evader Game"
    - "The Evolution of Communication Schemes Over Continuous Channels"

Connected through hyperedge HE-3 (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."
    - "Grounding Robotic Control with Genetic 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