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: "Simple Genetic Programming for Supervised Learning Problems"
  Abstract: This paper presents an evolutionary approach to finding learning rules to several supervised tasks. In this approach potential solutions are represented as variable length mathematical LISP S-expressions. Thus, it is similar to Genetic Programming (GP) but it employs a fixed set of non-problem-specific functions to solve a variety of problems. In this paper three Monk's and parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for supervised learning problems with the emphasis on hard learning problems. The problems and future research directions are discussed within the context of GP practices.

Connected through hyperedge HE-1 (induced by source paper "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Truth-from-Trash Learning and the Mobot"
    - "Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition"
    - "There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles"

Connected through hyperedge HE-2 (induced by source paper "Measuring the Difficulty of Specific Learning Problems"):
  with members:
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Interpretable Neural Networks with BP-SOM"
    - "Avoiding Overfitting with BP-SOM"
    - "Is Transfer Inductive?"

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