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: "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"
  Abstract: This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomized thermometer code generation technique can result in quantized datasets that when used to train simple perceptrons, can yield generalization on test data that is substantially better than that obtained with their unquantized counterparts.

Connected through hyperedge HE-1 (induced by source paper "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"):
  with members:
    - "Properties of Genetic Representations of Neural Architectures"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"

Connected through hyperedge HE-2 (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"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "Faster Learning in Multi-Layer Networks by Handling"

Connected through hyperedge HE-3 (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"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

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