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: "Co-Evolving Soccer Softbot Team Coordination with Genetic Programming"
  Abstract: Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, Genetic Programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. In this paper we present an overview of how we apply Genetic Programming to behavior-based team coordination in the RoboCup Soccer Server domain. The result is not just a hand-coded soccer algorithm, but a team of softbots which have learned on their own how to play a reasonable game of soccer.

Connected through hyperedge HE-1 (induced by source paper "Strongly Typed Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "A Methodology for Processing Problem Constraints in Genetic Programming"
    - "A Simulation of Adaptive Agents in a Hostile Environment"
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "Type Inheritance in Strongly Typed Genetic Programming"
    - "Modeling Distributed Search via Social Insects"
    - "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"

Connected through hyperedge HE-2 (induced by source paper "Team-Partitioned, Opaque-Transition Reinforcement Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "INCREMENTAL SELF-IMPROVEMENT FOR LIFE-TIME MULTI-AGENT REINFORCEMENT LEARNING"

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