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: "Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units"
  Abstract: We propose two algorithms for constructing and training compact feedforward networks of linear threshold units. The Shift procedure constructs networks with a single hidden layer while the PTI constructs multilayered networks. The resulting networks are guaranteed to perform any given task with binary or real-valued inputs. The various experimental results reported for tasks with binary and real inputs indicate that our methods compare favorably with alternative procedures deriving from similar strategies, both in terms of size of the resulting networks and of their generalization properties.

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"
    - "Maximizing the Robustness of a Linear Threshold Classifier with Discrete Weights"

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