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 "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"):
  Abstract: This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing elements. It examines learning mechanisms that constructively build up network structures that encode information from environmental stimuli at successively higher resolutions as needed for the tasks (e.g., perceptual recognition) that the network has to perform. Some simple examples are presented to illustrate the the basic structures and processes used in such networks to ensure the parsimony of learned representations by guiding the system to focus its efforts at the minimal adequate resolution. Several extensions of the basic algorithm for efficient learning using multi-resolution representations of spatial, temporal, or spatio-temporal patterns are discussed.

Includes members:
  Member "Symbolic and Subsymbolic Learning for Vision: Some Possibilities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Generative Learning Structures and Processes for Generalized Connectionist Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Properties of Genetic Representations of Neural Architectures")
  Member "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models")
  Member "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN")

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