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: "Generality versus Size in Genetic Programming"
  Abstract: Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game.

Connected through hyperedge HE-1 (induced by source paper "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Evolving Visual Routines"):
  with members:
    - "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"
    - "Evolving Visually Guided Robots"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"
    - "Evolving Edge Detectors with Genetic Programming edge detectors for 1-D signals and image profiles. The"

Connected through hyperedge HE-3 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Fast Online Q()"
    - "Modeling the Student with Reinforcement Learning"
    - "Learning in Continuous Domains with Delayed Rewards"

Connected through hyperedge HE-4 (induced by source paper "Generalization in Reinforcement Learning: Safely Approximating the Value Function"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Issues in Using Function Approximation for Reinforcement Learning"

Connected through hyperedge HE-5 (induced by source paper "Balancing Accuracy and Parsimony in Genetic Programming 1"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"

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