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: "SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)"
  Abstract: Predictability minimization (PM | Schmidhuber, 1992) exhibits various intuitive and theoretical advantages over many other methods for unsupervised redundancy reduction. So far, however, there were only toy applications of PM. In this paper, we apply semilinear PM to static real world images and find: without a teacher and without any significant preprocessing, the system automatically learns to generate distributed representations based on well-known feature detectors, such as orientation sensitive edge detectors and off-center-on-surround-like structures, thus extracting simple features related to those considered useful for image pre-processing and compression.

Connected through hyperedge HE-1 (induced by source paper "Analysis of Linsker's application of Hebbian rules to Linear Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "The Role of Constraints in Hebbian Learning"

Connected through hyperedge HE-2 (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"
    - "Plasticity-Mediated Competitive Learning"

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