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 "Viewpoint invariant face recognition using independent component analysis and attractor networks"):
  Abstract: We have explored two approaches to recognizing faces across changes in pose. First, we developed a representation of face images based on independent component analysis (ICA) and compared it to a principal component analysis (PCA) representation for face recognition. The ICA basis vectors for this data set were more spatially local than the PCA basis vectors and the ICA representation had greater invariance to changes in pose. Second, we present a model for the development of viewpoint invariant responses to faces from visual experience in a biological system. The temporal continuity of natural visual experience was incorporated into an attractor network model by Hebbian learning following a lowpass temporal filter on unit activities. When combined with the temporal filter, a basic Hebbian update rule became a generalization of Griniasty et al. (1993), which associates temporally proximal input patterns into basins of attraction. The system acquired rep resentations of faces that were largely independent of pose.

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 "Implicit learning in 3D object recognition: The importance of temporal context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A self-organizing multiple-view representation of 3D objects")
  Member "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A self-organizing multiple-view representation of 3D objects")
  Member "A Model of Invariant Object Recognition in the Visual System":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning Viewpoint Invariant Representations of Faces in an Attractor Network")

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