human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "The Effective Size of a Neural Network: A Principal Component Approach"):
  Abstract: Often when learning from data, one attaches a penalty term to a standard error term in an attempt to prefer simple models and prevent overfitting. Current penalty terms for neural networks, however, often do not take into account weight interaction. This is a critical drawback since the effective number of parameters in a network usually differs dramatically from the total number of possible parameters. In this paper, we present a penalty term that uses Principal Component Analysis to help detect functional redundancy in a neural network. Results show that our new algorithm gives a much more accurate estimate of network complexity than do standard approaches. As a result, our new term should be able to improve techniques that make use of a penalty term, such as weight decay, weight pruning, feature selection, Bayesian, and prediction-risk tech niques.

Includes members:
  Member "Irrelevant Features and the Subset Selection Problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all")
  Member "A Practical Bayesian Framework for Backprop Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Information-based objective functions for active data selection")
  Member "Using Sampling and Queries to Extract Rules from Trained Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Extracting Provably Correct Rules from Artificial Neural Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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