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: "Maximizing the Robustness of a Linear Threshold Classifier with Discrete Weights"
  Abstract: Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. An interesting property of neural networks used as classifiers is their ability to provide some robustness on input noise. This paper presents efficient learning algorithms for the maximization of the robustness of a Perceptron and especially designed to tackle the combinatorial problem arising from the discrete weights.

Connected through hyperedge HE-1 (induced by source paper "Constructive Training Methods for Feedforward Neural Networks with Binary Weights"):
  with members:
    - "Combining Linear Discriminant Functions with Neural Networks for Supervised Learning"
    - "Approximation with neural networks: Between local and global approximation"
    - "Orthogonal incremental learning of a feedforward network"
    - "Visual Tracking of Moving Objects using a Neural Network Controller"
    - "Predictive Robot Control with Neural Networks"
    - "The locally linear Nested Network for robot manipulation"
    - "Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units"

Connected through hyperedge HE-2 (induced by source paper "An evolutionary tabu search algorithm and the NHL scheduling problem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

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