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 self-organizing multiple-view representation of 3D objects"
  Abstract: We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

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 Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-2 (induced by source paper "Implicit learning in 3D object recognition: The importance of temporal context"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"
    - "A Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-3 (induced by source paper "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"):
  with members:
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"
    - "A Model of Invariant Object Recognition in the Visual System"

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