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 COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
  Abstract: Previous neural network learning algorithms for sequence processing are computationally expensive and perform poorly when it comes to long time lags. This paper first introduces a simple principle for reducing the descriptions of event sequences without loss of information. A consequence of this principle is that only unexpected inputs can be relevant. This insight leads to the construction of neural architectures that learn to `divide and conquer' by recursively decomposing sequences. I describe two architectures. The first functions as a self-organizing multi-level hierarchy of recurrent networks. The second, involving only two recurrent networks, tries to collapse a multi-level predictor hierarchy into a single recurrent net. Experiments show that the system can require less computation per time step and many fewer training sequences than conventional training algorithms for recurrent nets.

Connected through hyperedge HE-1 (induced by source paper "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"):
  with members:
    - "Neural net architectures for temporal sequence processing"
    - "A `SELF-REFERENTIAL' WEIGHT MATRIX"
    - "Locally Connected Recurrent Networks"

Connected through hyperedge HE-2 (induced by source paper "LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992)"):
  with members:
    - "A `SELF-REFERENTIAL' WEIGHT MATRIX"

Connected through hyperedge HE-3 (induced by source paper "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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