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 "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.

Includes members:
  Member "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997")
  Member "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Locally Connected Recurrent Networks")
  Member "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain")
  Member "LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992)":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A `SELF-REFERENTIAL' WEIGHT MATRIX")

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