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: "Neural Programming and an Internal Reinforcement Policy"
  Abstract: An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerful parameterized primitive constructs. Unlike ANNs, though, GP does not have such a principled procedure for changing parts of the learned system based on its current performance. This paper introduces Neural Programming, a connectionist representation for evolving programs that maintains the benefits of GP. The connectionist model of Neural Programming allows for a regression credit-blame procedure in an evolutionary learning system. We describe a general method for an informed feedback mechanism for Neural Programming, Internal Reinforcement. We introduce an Internal Reinforcement procedure and demon strate its use through an illustrative experiment.

Connected through hyperedge HE-1 (induced by source paper "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"):
  with members:
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "The Impact of External Dependency in Genetic Programming Primitives"
    - "Using a Distance Metric on Genetic Programs to Understand Genetic Operators"
    - "Where Does the Good Stuff Go, and Why? How contextual semantics influences program structure in"

Connected through hyperedge HE-2 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Evolving Teamwork and Coordination with Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"

Connected through hyperedge HE-3 (induced by source paper "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"):
  with members:
    - "Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming"
    - "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"
    - "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