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: "NIPS*97 Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring"
  Abstract: For blind source separation, when the Fisher information matrix is used as the Riemannian metric tensor for the parameter space, the steepest descent algorithm to maximize the likelihood function in this Riemannian parameter space becomes the serial updating rule with equivariant property. This algorithm can be further simplified by using the asymptotic form of the Fisher information matrix around the equilibrium.

Connected through hyperedge HE-1 (induced by source paper "A New Learning Algorithm for Blind Signal Separation"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "Equivariant adaptive source separation"
    - "NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES"
    - "A Blind Identification and Separation Technique via Multi-layer Neural Networks"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "LEARNING LINEAR, SPARSE, FACTORIAL CODES"
    - "Analyzing Hyperspectral Data with Independent Component Analysis"

Connected through hyperedge HE-2 (induced by source paper "Equivariant adaptive source separation"):
  with members:
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES"
    - "A Blind Identification and Separation Technique via Multi-layer Neural Networks"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "Principal and Independent Components in Neural Networks Recent Developments"
    - "Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model 1"
    - "Edges are the `Independent Components' of Natural Scenes."

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