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: "Predicting Time Series with Support Vector Machines"
  Abstract: Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an * insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 37%.

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"
    - "Nonlinear Component Analysis as a Kernel Eigenvalue Problem"

Connected through hyperedge HE-2 (induced by source paper "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"):
  with members:
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models"

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: Theory