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 "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"):
  Abstract: Novel on-line learning algorithms with self adaptive learning rates (parameters) for blind separation of signals are proposed. The main motivation for development of new learning rules is to improve convergence speed and to reduce cross-talking, especially for non-stationary signals. Furthermore, we have discovered that under some conditions the proposed neural network models with associated learning algorithms exhibit a random switch of attention, i.e. they have ability of chaotic or random switching or cross-over of output signals in such way that a specified separated signal may appear at various outputs at different time windows. Validity, performance and dynamic properties of the proposed learning algorithms are investigated by computer simulation experiments.

Includes members:
  Member "An information-maximisation approach to blind separation and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")
  Member "A New Learning Algorithm for Blind Signal Separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")
  Member "Equivariant adaptive source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")
  Member "NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")
  Member "A Blind Identification and Separation Technique via Multi-layer Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")
  Member "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")

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