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: "The locally linear Nested Network for robot manipulation"
  Abstract: We present a method for accurate representation of high-dimensional unknown functions from random samples drawn from its input space. The method builds representations of the function by recursively splitting the input space in smaller subspaces, while in each of these subspaces a linear approximation is computed. The representations of the function at all levels (i.e., depths in the tree) are retained during the learning process, such that a good generalisation is available as well as more accurate representations in some subareas. Therefore, fast and accurate learning are combined in this method.

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"
    - "Maximizing the Robustness of a Linear Threshold Classifier with Discrete Weights"
    - "Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units"

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