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: "Towards Automatic Discovery of Building Blocks in Genetic Programming"
  Abstract: This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by extending the set of terminals and functions with a set of evolvable subroutines. The set of subroutines extracts common knowledge emerging during the evolutionary process and acquires the necessary structure for solving the problem. ARL supports subroutine creation and deletion. Subroutine creation or discovery is performed automatically based on the differential parent-offspring fitness and block activation. Subroutine deletion relies on a utility measure similar to schema fitness over a window of past generations. The technique described is tested on the problem of controlling an agent in a dynamic and non-deterministic environment. The automatic discovery of subroutines can help scale up the GP technique to complex problems.

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 "Strongly Typed Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "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++"
    - "Program Optimization for Faster Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing"
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "The Schema Theorem and Price's Theorem"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

Connected through hyperedge HE-4 (induced by source paper "Causality in Genetic Programming"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Genetic Programming and Redundancy"
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Hierarchical Self-Organization in Genetic Programming"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"

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