human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Exploiting Tractable Substructures in Intractable Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View")
  Member "A self-organizing multiple-view representation of 3D objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning Viewpoint Invariant Representations of Faces in an Attractor Network")
  Member "Viewpoint invariant face recognition using independent component analysis and attractor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Model of Invariant Object Recognition in the Visual System")
  Member "A Model of Invariant Object Recognition in the Visual System":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning Viewpoint Invariant Representations of Faces in an Attractor Network")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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