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: "Nonlinear Component Analysis as a Kernel Eigenvalue Problem"
  Abstract: A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible 5-pixel products in 16fi16 images. We give the derivation of the method and present first experimental results on polynomial feature extraction for pattern recognition.

Connected through hyperedge HE-1 (induced by source paper "Extracting Support Data for a Given Task"):
  with members:
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Improving the Accuracy and Speed of Support Vector Machines"
    - "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"
    - "Support Vector Machines: Training and Applications"
    - "Predicting Time Series with Support Vector Machines"

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