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 "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"):
  Abstract: We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We derive a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.

Includes members:
  Member "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification")
  Member "Feature Extraction Using an Unsupervised Neural Network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On Centering Neural Network Weight Updates")
  Member "On Centering Neural Network Weight Updates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network")
  Member "Centering Neural Network Gradient Factors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network")
  Member "Empirical Entropy Manipulation for Real-World Problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution")
  Member "Plasticity-Mediated Competitive Learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "A Non-linear Information Maximisation Algorithm that Performs Blind 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