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: "Evolution-based Discovery of Hierarchical Behaviors"
  Abstract: Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate modularization. In this paper we compare several randomized algorithms for learning modular procedural representations. The main algorithm, called Adaptive Representation through Learning (ARL) is a genetic programming extension that relies on the discovery of subroutines. ARL is suitable for learning hierarchies of subroutines and for constructing policies to complex tasks. ARL was successfully tested on a typical reinforcement learning problem of controlling an agent in a dynamic and nondeterministic environment where the discovered subroutines correspond to agent behaviors.

Connected through hyperedge HE-1 (induced by source paper "Genetic Programming Exploratory Power and the Discovery of Functions"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Causality in Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "The Schema Theorem and Price's Theorem"
    - "Genetic Programming with One-Point Crossover and Point Mutation"
    - "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Evaluation and Selection of Biases in Machine Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "#1 Robust Feature Selection Algorithms"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Model selection using measure functions"
    - "Dlab: A Declarative Language Bias Formalism"
    - "Utilizing Prior Concepts for 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