human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mixed precision training of deep neural networks using computational memory"):
  Abstract: Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speech recognition. Training of large DNNs, however, is a computationally intensive task, and this necessitates the development of novel computing architectures targeting this application. A computational memory unit where resistive memory devices are organized in crossbar arrays can be used to locally store the synaptic weights in their conductance states. The expensive multiply accumulate operations can be performed in place using Kirchhoff's circuit laws in a non-von Neumann manner. However, a key challenge remains the inability to alter the conductance states of the devices in a reliable manner during the weight update process. We propose a mixed-precision architecture that combines a computational memory unit storing the synaptic weights with a digital processing unit and an additional memory unit accumulating weight updates in high precision. The new architecture delivers classification accuracies comparable to those of floating-point implementations without being constrained by challenges associated with the non-ideal weight update characteristics of emerging resistive memories. A two layer neural network in which the computational memory unit is realized using non-linear stochastic models of phase-change memory devices achieves a test accuracy of 97.40% on the MNIST handwritten digit classification problem.

Includes members:
  Member "mixed precision in memory computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "analog cmos based resistive processing unit for deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "deep neural networks are robust to weight binarization and other non linear distortions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "buddy ram improving the performance and efficiency of bulk bitwise operations using dram":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "acceleration of deep neural network training with resistive cross point devices design considerations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "rounding methods for neural networks with low resolution synaptic weights":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural ordinary differential equations")
  Member "deep learning with limited numerical precision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.ET(Emerging Technologies)