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: "Extracting Support Data for a Given Task"
  Abstract: We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small ( 4%) subsets of the data base. This finding opens up the possibility of compressing data bases significantly by disposing of the data which is not important for the solution of a given task. In addition, we show that the theory allows us to predict the classifier that will have the best generalization ability, based solely on performance on the training set and characteristics of the learning machines. This finding is important for cases where the amount of available data is limited.

Connected through hyperedge HE-1 (induced by source paper "Support Vector Machines: Training and Applications"):
  with members:
    - "Massive Data Discrimination via Linear Support Vector Machines"
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "Human Face Detection in Visual Scenes"

Connected through hyperedge HE-2 (induced by source paper "Predicting Time Series with Support Vector Machines"):
  with members:
    - "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"

Connected through hyperedge HE-3 (induced by source paper "Improving the Accuracy and Speed of Support Vector Machines"):
  with members:
    - "A Support Vector Machine Approach to Decision Trees"
    - "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"
    - "Incorporating Invariances in Support Vector Learning Machines"

Connected through hyperedge HE-4 (induced by source paper "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Improving the Accuracy and Speed of Support Vector Machines"
    - "Incorporating Invariances in Support Vector Learning Machines"

Connected through hyperedge HE-5 (induced by source paper "Unsupervised Learning by Convex and Conic Coding"):
  with members:
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "Pattern analysis and synthesis in attractor neural networks"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "Factorial Learning and the EM Algorithm"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"

Connected through hyperedge HE-6 (induced by source paper "Nonlinear Component Analysis as a Kernel Eigenvalue Problem"):
  with members:
    (none)

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