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 "rxnn a framework for evaluating deep neural networks on resistive crossbars"):
  Abstract: Resistive crossbars have emerged as promising building blocks for realizing DNNs due to their ability to compactly and efficiently realize the dominant DNN computational kernel, viz., vector-matrix multiplication. However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level non-idealities such as interconnect parasitics, peripheral circuits, sneak paths, and process variations. These non-idealities can lead to errors in vector-matrix multiplication that eventually degrade the DNN's accuracy. There has been no study of the impact of non-idealities on the accuracy of large-scale DNNs, in part because existing device and circuit models are infeasible to use in application-level evaluation. In this work, we present a fast and accurate simulation framework to enable evaluation and re-training of large-scale DNNs on resistive crossbar based hardware fabrics. #R##N#We first characterize the impact of crossbar non-idealities on errors incurred in the realized vector-matrix multiplications and observe that the errors have significant data and hardware-instance dependence that should be considered. We propose a Fast Crossbar Model (FCM) to accurately capture the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation. Finally, we develop RxNN, a software framework to evaluate and re-train DNNs on resistive crossbar systems. RxNN is based on the popular Caffe machine learning framework, and we use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge (ILSVRC). Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6%-32%) for these large-scale DNNs. To the best of our knowledge, this work is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware.

Includes members:
  Member "group scissor scaling neuromorphic computing design to large neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array")
  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 "a survey of neuromorphic computing and neural networks in hardware":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "training deep convolutional neural networks with resistive cross point devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cmos free multilayer perceptron enabled by four terminal mtj device")
  Member "acceleration of deep neural network training with resistive cross point devices design considerations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "stt snn a spin transfer torque based soft limiting non linear neuron for low power artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spin orbit torque based devices circuits and architectures")
  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)