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: "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.

Connected through hyperedge HE-1 (induced by source paper "Neuronal Goals: Efficient Coding and Coincidence Detection"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Natural image statistics and efficient coding"
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"

Connected through hyperedge HE-2 (induced by source paper "BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment."):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Natural image statistics and efficient coding"

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