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 "Adaptive Noise Injection for Input Variables Relevance Determination"):
  Abstract: In this paper we consider the application of training with noise in multi-layer perceptron to input variables relevance determination. Noise injection is modified in order to penalize irrelevant features. The proposed algorithm is attractive as it requires the tuning of a single parameter. This parameter controls the penalization of the inputs together with the complexity of the model. After the presentation of the method, experimental evidences are given on simulated data sets.

Includes members:
  Member "Flexible Metric Nearest Neighbor Classiflcation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning")
  Member "From Data Distributions to Regularization in Invariant Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Face Recognition: A Hybrid Neural Network Approach")
  Member "Least Absolute Shrinkage is Equivalent to Quadratic Penalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A comparison of some error estimates for neural network models  Summary")

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