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 "Inferring sparse, overcomplete image codes using an efficient coding framework"):
  Abstract: We apply a general technique for learning overcomplete bases to the problem of finding efficient image codes. The bases learned by the algorithm are localized, oriented, and bandpass, consistent with earlier results obtained using related methods. We show that the learned bases are Gabor-like in structure and that higher degrees of overcompleteness produce greater sampling density in position, orientation, and scale. The efficient coding framework provides a method for comparing different bases objectively by calculating their probability given the observed data or by measuring the entropy of the basis function coefficients. Compared to complete and overcomplete Fourier and wavelet bases, the learned bases have much better coding efficiency. We demonstrate the improvement in the representation of the learned bases by showing superior performance in image denoising and filling-in of missing pixels.

Includes members:
  Member "An information-maximisation approach to blind separation and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")

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