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 "Finding Compact and Sparse Distributed Representations of Visual Images"):
  Abstract: Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neural network which self organises on the basis of simple Hebbian learning and negative feedback of activation and show that it is capable of both forming compact codings of data distributions and also of identifying filters most sensitive to sparse distributed codes. The network is extremely simple and its biological relevance is investigated via its response to a set of images which are typical of everyday life. However, an analysis of the network's identification of the filter for sparse coding reveals that this coding may not be globally optimal and that there exists an innate limiting factor which cannot be transcended.

Includes members:
  Member "Neuronal Goals: Efficient Coding and Coincidence Detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network")
  Member "BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment.":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Natural image statistics and efficient coding")

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