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 "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"):
  Abstract: In this paper a neural network approach for reconstruction of natural highly correlated images from linear (additive) mixture of them is proposed. A multi-layer architecture with local on-line learning rules is developed to solve the problem of blind separation of sources. The main motivation for using a multi-layer network instead of a single-layer one is to improve the performance and robustness of separation, while applying a very simple local learning rule, which is biologically plausible. Moreover such architecture with on-chip learning is relatively easy implementable using VLSI electronic circuits. Furthermore it enables the extraction of source signals sequentially one after the other, starting from the strongest signal and finishing with the weakest one. The experimental part focuses on separating highly correlated human faces from mixture of them, with additive noise and under unknown number of sources.

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")
  Member "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")
  Member "A New Learning Algorithm for Blind Signal Separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")
  Member "Equivariant adaptive source separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")

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