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: "Implicit learning in 3D object recognition: The importance of temporal context"
  Abstract: A novel architecture and set of learning rules for cortical self-organization is proposed. The model is based on the idea that multiple information channels can modulate one another's plasticity. Features learned from bottom-up information sources can thus be influenced by those learned from contextual pathways, and vice versa. A maximum likelihood cost function allows this scheme to be implemented in a biologically feasible, hierarchical neural circuit. In simulations of the model, we first demonstrate the utility of temporal context in modulating plasticity. The model learns a representation that categorizes people's faces according to identity, independent of viewpoint, by taking advantage of the temporal continuity in image sequences. In a second set of simulations, we add plasticity to the contextual stream and explore variations in the architecture. In this case, the model learns a two-tiered representation, starting with a coarse view-based clustering and proceeding to a finer clustering of more specific stimulus features. This model provides a tenable account of how people may perform 3D object recognition in a hierarchical, bottom-up fashion.

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"
    - "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"
    - "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"
    - "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"

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"
    - "Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association"
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"

Connected through hyperedge HE-4 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Learning in Boltzmann Trees"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Tractable Inference for Complex Stochastic Processes"

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