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: "Simulation of Reduced Precision Arithmetic for Digital Neural Networks Using the RAP Machine"
  Abstract: This paper describes some of our recent work in the development of computer architectures for efficient execution of artificial neural network algorithms. Our earlier system, the Ring Array Processor (RAP), was a multiprocessor based on commercial DSPs with a low-latency ring interconnection scheme. We have used the RAP to simulate variable precision arithmetic and guide us in the design of higher performance neurocomputers based on custom VLSI. The RAP system played a critical role in this study, enabling us to experiment with much larger networks than would otherwise be possible. Our study shows that back-propagation training algorithms only require moderate precision. Specifically, 16b weight values and 8b output values are sufficient to achieve training and classification results comparable to 32b floating point. Although these results were gathered for frame classification in continuous speech, we expect that they will extend to many other connectionist calculations. We have used these results as part of the design of a programmable single chip microprocessor, SPERT. The reduced precision arithmetic permits the use of multiple units per processor. Also, reduced precision operands make more efficient use of valuable processor-memory bandwidth. For our moderate-precision fixed-point arithmetic applications, SPERT represents more than an order of magnitude reduction in cost over systems based on DSP chips.

Connected through hyperedge HE-1 (induced by source paper "Connectionist Layered Object-Oriented Network Simulator (CLONES): User's Manual minimize the learning curve for using CLONES,"):
  with members:
    - "SPRU034A. </note>"
    - "ICSIM: An Object-Oriented Connectionist Simulator gives an overview of the simulator. Its main concepts, the"
    - "SPERT: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations"
    - "A Symbolic Complexity Analysis of Connectionist Algorithms for Distributed-Memory Machines"

Connected through hyperedge HE-2 (induced by source paper "SPERT: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations"):
  with members:
    - "Connectionist Layered Object-Oriented Network Simulator (CLONES): User's Manual minimize the learning curve for using CLONES,"
    - "Object Oriented Design of a BP Neural Network Simulator and Implementation on the Connection Machine (CM-5)"

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