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 Neural Network Based Head Tracking System"
  Abstract: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to the neural network consist of normalized luminance and chrominance images and motion information from frame differences. Subsampled images are also used to provide scale invariance. During the online training phase, the neural network rapidly adjusts the input weights depending upon the reliability of the different channels in the surrounding environment. This quick adaptation allows the system to robustly track a head even when other objects are moving within a cluttered background.

Connected through hyperedge HE-1 (induced by source paper "Human Face Detection in Visual Scenes"):
  with members:
    - "Face Recognition: A Hybrid Neural Network Approach"
    - "Support Vector Machines: Training and Applications"

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