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 "Learning Viewpoint Invariant Representations of Faces in an Attractor Network"):
  Abstract: In natural visual experience, different views of an object tend to appear in close temporal proximity as an animal manipulates the object or navigates around it. We investigated the ability of an attractor network to acquire view invariant visual representations by associating first neighbors in a pattern sequence. The pattern sequence contains successive views of faces of ten individuals as they change pose. Under the network dynamics developed by Griniasty, Tsodyks & Amit (1993), multiple views of a given subject fall into the same basin of attraction. We use an independent component (ICA) representation of the faces for the input patterns (Bell & Sejnowski, 1995). The ICA representation has advantages over the principal component representation (PCA) for viewpoint-invariant recognition both with and without the attractor network, suggesting that ICA is a better representation than PCA for object recognition.

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 "A self-organizing multiple-view representation of 3D objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Implicit learning in 3D object recognition: The importance of temporal context")
  Member "A Model of Invariant Object Recognition in the Visual System":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Viewpoint invariant face recognition using independent component analysis and attractor 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