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 of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"
  Abstract: Technical Report: CSRP-97-5 School of Computer Science The University of Birmingham Abstract In this paper we present an approach to the interactive development of programs for image enhancement with Genetic Programming (GP) based on pseudo-colour transformations. In our approach the user drives GP by deciding which individual should be the winner in tournament selection. The presence of the user does not only allow running GP without a fitness function but it also transforms GP into a very efficient search procedure capable of producing effective solutions to real-life problems in only hundreds of evaluations. In the paper we also propose a strategy to further reduce user interaction: we record the choices made by the user in interactive runs and we later use them to build a model which can replace him/her in longer runs. Experimental results with interactive GP and with our user-modelling strategy are also reported.

Connected through hyperedge HE-1 (induced by source paper "Evolving Visual Routines"):
  with members:
    - "Evolving Visually Guided Robots"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"
    - "Generality versus Size in Genetic Programming"
    - "Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The"

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"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-3 (induced by source paper "Induction and Recapitulation of Deep Musical Structure"):
  with members:
    - "Entailment for Specification Refinement"
    - "GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters"
    - "Automated Fitness Raters for the GP-Music System"

Connected through hyperedge HE-4 (induced by source paper "Program Optimization for Faster Genetic Programming"):
  with members:
    - "Strongly Typed Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Cellular Encoding for Interactive Evolutionary Robotics"):
  with members:
    (none)

Connected through hyperedge HE-6 (induced by source paper "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"):
  with members:
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
    - "Neural Programming and an Internal Reinforcement Policy"
    - "Some Steps Towards a Form of Parallel Distributed Genetic Programming"

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