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: "A Model of Invariant Object Recognition in the Visual System"
  Abstract: Neurons in the ventral stream of the primate visual system exhibit responses to the images of objects which are invariant with respect to natural transformations such as translation, size, and view. Anatomical and neurophysiological evidence suggests that this is achieved through a series of hierarchical processing areas. In an attempt to elucidate the manner in which such representations are established, we have constructed a model of cortical visual processing which seeks to parallel many features of this system, specifically the multi-stage hierarchy with its topologically constrained convergent connectivity. Each stage is constructed as a competitive network utilising a modified Hebb-like learning rule, called the trace rule, which incorporates previous as well as current neuronal activity. The trace rule enables neurons to learn about whatever is invariant over short time periods (e.g. 0.5 s) in the representation of objects as the objects transform in the real world. The trace rule enables neurons to learn the statistical invariances about objects during their transformations, by associating together representations which occur close together in time. We show that by using the trace rule training algorithm the model can indeed learn to produce transformation invariant responses to natural stimuli such as faces.

Connected through hyperedge HE-1 (induced by source paper "Learning Viewpoint Invariant Representations of Faces in an Attractor Network"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "A self-organizing multiple-view representation of 3D objects"

Connected through hyperedge HE-2 (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"
    - "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"

Connected through hyperedge HE-3 (induced by source paper "Implicit learning in 3D object recognition: The importance of temporal context"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "A self-organizing multiple-view representation of 3D objects"
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"

Connected through hyperedge HE-4 (induced by source paper "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"):
  with members:
    - "A self-organizing multiple-view representation of 3D objects"
    - "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