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: "Determining Successful Negotiation Strategies: An Evolutionary Approach"
  Abstract: To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opponent in different environments. In particular, we adopt an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent's opponent.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Selection Schemes used in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Overview of Selection Schemes and a Suggested Classification"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (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"
    - "Genetic Programming and Redundancy"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

Connected through hyperedge HE-3 (induced by source paper "Genetic Algorithms, Tournament Selection, and the Effects of Noise"):
  with members:
    (none)

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