human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"
  Abstract: In natural visual experience, different views of an object or face tend to appear in close temporal proximity. A set of simulations is presented which demonstrate how viewpoint invariant representations of faces can be developed from visual experience by capturing the temporal relationships among the input patterns. The simulations explored the interaction of temporal smoothing of activity signals with Hebbian learning (Foldiak, 1991) in both a feed-forward system and a recurrent system. The recurrent system was a generalization of a Hopfield network with a lowpass temporal filter on all unit activities. Following training on sequences of graylevel images of faces as they changed pose, multiple views of a given face fell into the same basin of attraction, and the system acquired representations of faces that were approximately viewpoint invariant.

Connected through hyperedge HE-1 (induced by source paper "Viewpoint invariant face recognition using independent component analysis and attractor networks"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "A Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-2 (induced by source paper "A self-organizing multiple-view representation of 3D objects"):
  with members:
    - "Learning Viewpoint Invariant Representations of Faces in an Attractor Network"
    - "Implicit learning in 3D object recognition: The importance of temporal context"

Connected through hyperedge HE-3 (induced by source paper "A Model of Invariant Object Recognition in the Visual System"):
  with members:
    - "Learning Viewpoint Invariant Representations of Faces in an Attractor Network"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"

Question: Please tell me which class the center node 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