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: "Approximation with neural networks: Between local and global approximation"
  Abstract: We investigate neural network based approximation methods. These methods depend on the locality of the basis functions. After discussing local and global basis functions, we propose a a multi-resolution hierarchical method. The various resolutions are stored at various levels in a tree. At the root of the tree, a global approximation is kept; the leafs store the learning samples themselves. Intermediate nodes store intermediate representations. In order to find an optimal partitioning of the input space, self-organising maps (SOM's) are used. The proposed method has implementational problems reminiscent of those encountered in many-particle simulations. We will investigate the parallel implementation of this method, using parallel hierarchical meth ods for many-particle simulations as a starting point.

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"
    - "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"
    - "Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units"

Connected through hyperedge HE-2 (induced by source paper "Using Many-Particle Decomposition to get a Parallel Self-Organising Map"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"

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