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: "Incorporating Invariances in Support Vector Learning Machines"
  Abstract: Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invari-ances by applying transformations to support vectors, the training ex amples most critical for determining the classification boundary.

Connected through hyperedge HE-1 (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"
    - "Extracting Support Data for a Given Task"

Connected through hyperedge HE-2 (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"
    - "Extracting Support Data for a Given Task"

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