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 "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"):
  Abstract: The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics, and phase change materials have been explored to implement the core functional units of neuromorphic systems, namely the synaptic network, and the neuronal functionality, in a fast and energy efficient manner. However, various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks, and hence, impose restrictions on feasible crossbar sizes. In this paper, we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance, neuron resistance, and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network (FCN) and convolutional neural network (CNN) trained with Backpropagation algorithm. We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84% for FCNs and 62.4% for CNNs when implemented on nonideal crossbars for relevant nonideality ranges. This severely constrains the sizes for crossbars. As a solution, we propose a technology aware training algorithm, which incorporates the mathematical models of the nonidealities in the backpropagation algorithm. We demonstrate that our proposed methodology achieves significant recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and 1.5% for CNNs. We further show that our proposed training algorithm can potentially allow the use of significantly larger crossbar arrays of sizes 784 × 500 for FCNs and 4096 × 512 for CNNs with a minor or no tradeoff in accuracy.

Includes members:
  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-1 (induced by source paper "cmos free multilayer perceptron enabled by four terminal mtj device")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "π nets deep polynomial neural networks")
  Member "training and operation of an integrated neuromorphic network based on metal oxide memristors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "brain like associative learning using a nanoscale non volatile phase change synaptic device array":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks")

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)