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: "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
  Abstract: Constructive learning algorithms offer an approach to incremental construction of near-minimal artificial neural networks for pattern classification. Examples of such algorithms include Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer networks of threshold logic units (or, multilayer perceptrons). These algorithms differ in terms of the topology of the networks that they construct which in turn biases the search for a decision boundary that correctly classifies the training set. This paper presents an analysis of such algorithms from a geometrical perspective. This analysis helps in a better characterization of the search bias employed by the different algorithms in relation to the geometrical distribution of examples in the training set. Simple experiments with non linearly separable training sets support the results of mathematical analysis of such algorithms. This suggests the possibility of designing more efficient constructive algorithms that dynamically choose among different biases to build near-minimal networks for pattern classification.

Connected through hyperedge HE-1 (induced by source paper "Properties of Genetic Representations of Neural Architectures"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"

Connected through hyperedge HE-2 (induced by source paper "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"):
  with members:
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"

Connected through hyperedge HE-3 (induced by source paper "Generative Learning Structures and Processes for Generalized Connectionist Networks"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Properties of Genetic Representations of Neural Architectures"
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "Faster Learning in Multi-Layer Networks by Handling"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"):
  with members:
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Experiments with the Cascade-Correlation Algorithm"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

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: Neural_Networks