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: "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
  Abstract: I propose a novel general principle for unsupervised learning of distributed non-redundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter `abstract concepts' out of the environmental input such that these concepts are statistically independent of those upon which the other units focus. I discuss various simple yet potentially powerful implementations of the principle which aim at finding binary factorial codes (Bar-low et al., 1989), i.e. codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also non-linear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"):
  with members:
    - "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"
    - "Plasticity-Mediated Competitive Learning"

Connected through hyperedge HE-2 (induced by source paper "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"):
  with members:
    - "VISIT: An Efficient Computational Model of Human Visual Attention"
    - "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."
    - "Local Multivariate Binary Processors"
    - "Book Review New Kids on the Block way in the field of connectionist modeling. The"

Connected through hyperedge HE-3 (induced by source paper "Plasticity-Mediated Competitive Learning"):
  with members:
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
    - "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."

Connected through hyperedge HE-4 (induced by source paper "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"):
  with members:
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"
    - "LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992)"

Connected through hyperedge HE-5 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution"):
  with members:
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "Analyzing Hyperspectral Data with Independent Component Analysis"
    - "Independent Component Analysis of Electroencephalographic Data"
    - "Principal and Independent Components in Neural Networks Recent Developments"

Connected through hyperedge HE-6 (induced by source paper "SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)"):
  with members:
    - "Analysis of Linsker's application of Hebbian rules to Linear Networks"

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