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: "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.

Connected through hyperedge HE-1 (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"
    - "A Model of Invariant Object Recognition in the Visual System"

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

Connected through hyperedge HE-3 (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"
    - "A Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-4 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution"):
  with members:
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "Analyzing Hyperspectral Data with Independent Component Analysis"
    - "Independent Component Analysis of Electroencephalographic Data"

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