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: "On the performance of orthogonal source separation algorithms"
  Abstract: Source separation consists in recovering a set of n independent signals from m n observed instantaneous mixtures of these signals, possibly corrupted by additive noise. Many source separation algorithms use second order information in a whitening operation which reduces the non trivial part of the separation to determining a unitary matrix. Most of them further show a kind of invariance property which can be exploited to predict some general results about their performance. Our first contribution is to exhibit a lower bound to the performance in terms of accuracy of the separation. This bound is independent of the algorithm and, in the i.i.d. case, of the distribution of the source signals. Second, we show that the performance of invariant algorithms depends on the mixing matrix and on the noise level in a specific way. A consequence is that at low noise levels, the performance does not depend on the mixture but only on the distribution of the sources, via a function which is characteristic of the given source separation algorithm.

Connected through hyperedge HE-1 (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."

Connected through hyperedge HE-2 (induced by source paper "Maximum likelihood source separation for discrete sources"):
  with members:
    - "Equivariant adaptive source separation"

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