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: "Evolving Visual Routines"
  Abstract: Traditional machine vision assumes that the vision system recovers a a complete, labeled description of the world [ Marr, 1982 ] . Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [ Aloimonos, 1993, Ullman, 1987 ] . Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [ Ramachandran, 1985 ] . So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g. [ Chapman, 1993 ] ). In this paper we propose an alternative approach in which visual routines for simple tasks are evolved using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using Genetic Programming techniques [ Koza, 1992 ] . The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the best algorithm we were able to write by hand.

Connected through hyperedge HE-1 (induced by source paper "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"

Connected through hyperedge HE-2 (induced by source paper "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Program Optimization for Faster Genetic Programming"
    - "Induction and Recapitulation of Deep Musical Structure"
    - "Cellular Encoding for Interactive Evolutionary Robotics"
    - "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The"):
  with members:
    - "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"

Connected through hyperedge HE-4 (induced by source paper "Generality versus Size in Genetic Programming"):
  with members:
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"
    - "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited"

Connected through hyperedge HE-5 (induced by source paper "Evolving Visually Guided Robots"):
  with members:
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Issues in Evolutionary Robotics"

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