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 Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"
  Abstract: In drug activity prediction (as in handwritten character recognition), the features extracted to describe a training example depend on the pose (location, orientation, etc.) of the example. In handwritten character recognition, one of the best techniques for addressing this problem is the tangent distance method of Simard, LeCun and Denker (1993). Jain, et al. (1993a; 1993b) introduce a new technique|dynamic reposing|that also addresses this problem. Dynamic reposing iteratively learns a neural network and then reposes the examples in an effort to maximize the predicted output values. New models are trained and new poses computed until models and poses converge. This paper compares dynamic reposing to the tangent distance method on the task of predicting the biological activity of musk compounds. In a 20-fold cross-validation,

Connected through hyperedge HE-1 (induced by source paper "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"):
  with members:
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Generalizing from Case Studies: A Case Study"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"
    - "Lookahead and Discretization in ILP"

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