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: "Plasticity-Mediated Competitive Learning"
  Abstract: Differentiation between the nodes of a competitive learning network is conventionally achieved through competition on the basis of neural activity. Simple inhibitory mechanisms are limited to sparse representations, while decorrelation and factorization schemes that support distributed representations are computation-ally unattractive. By letting neural plasticity mediate the competitive interaction instead, we obtain diffuse, nonadaptive alternatives for fully distributed representations. We use this technique to simplify and improve our binary information gain optimization algorithm for feature extraction (Schraudolph and Sejnowski, 1993); the same approach could be used to improve other learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"):
  with members:
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "On Centering Neural Network Weight Updates"
    - "Centering Neural Network Gradient Factors"
    - "Empirical Entropy Manipulation for Real-World Problems"

Connected through hyperedge HE-2 (induced by source paper "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"

Connected through hyperedge HE-3 (induced by source paper "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
    - "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"
    - "SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)"

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