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 "A Performance Analysis of the CNS-1 on Large, Dense Backpropagation Networks Connectionist Network Supercomputer"):
  Abstract: We determine in this study the sustained performance of the CNS-1 during training and evaluation of large multilayered feedforward neural networks. Using a sophisticated coding, the 128-node machine would achieve up to 111 Giga connections per second (GCPS) and 22 Giga connection updates per second (GCUPS). During recall the machine would archieve 87% of the peak multiply-accumulate performance. The training of large nets is less efficient than the recall but only by a factor of 1.5 to 2. The benchmark is parallelized and the machine code is optimized before analyzing the performance. Starting from an optimal parallel algorithm, CNS specific optimizations still reduce the run time by a factor of 4 for recall and by a factor of 3 for training. Our analysis also yields some strategies for code optimization. The CNS-1 is still in design, and therefore we have to model the run time behavior of the memory system and the interconnection network. This gives us the option of changing some parameters of the CNS-1 system in order to analyze their performance impact.

Includes members:
  Member "A Performance Analysis of CNS-1 on Sparse Connectionist Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "All-to-all Broadcast on the CNS-1")
  Member "All-to-all Broadcast on the CNS-1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Performance Analysis of CNS-1 on Sparse Connectionist Networks")

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