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 "an ultra efficient memristor based dnn framework with structured weight pruning and quantization using admm"):
  Abstract: The high computation and memory storage of large deep neural networks (DNNs) models pose intensive challenges to the conventional Von-Neumann architecture, incurring substantial data movements in the memory hierarchy. The memristor crossbar array has emerged as a promising solution to mitigate the challenges and enable low-power acceleration of DNNs. Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model. However, there has been no systematic investigation of memristor-based neuromorphic computing (NC) systems considering both weight pruning and weight quantization. In this paper, we propose an unified and systematic memristor-based framework considering both structured weight pruning and weight quantization by incorporating alternating direction method of multipliers (ADMM) into DNNs training. We consider hardware constraints such as crossbar blocks pruning, conductance range, and mismatch between weight value and real devices, to achieve high accuracy and low power and small area footprint. Our framework is mainly integrated by three steps, i.e., memristor-based ADMM regularized optimization, masked mapping and retraining. Experimental results show that our proposed framework achieves 29.81X (20.88X) weight compression ratio, with 98.38% (96.96%) and 98.29% (97.47%) power and area reduction on VGG-16 (ResNet-18) network where only have 0.5% (0.76%) accuracy loss, compared to the original DNN models. We share our models at link this http URL

Includes members:
  Member "26ms inference time for resnet 50 towards real time execution of all dnns on smartphone":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the tradeoff between coverage and computation in wireless networks")
  Member "toward extremely low bit and lossless accuracy in dnns with progressive admm":
    also connected through other hyperedges:
      (none)
  Member "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "resnet can be pruned 60x introducing network purification and unused path removal p rm after weight pruning":
    also connected through other hyperedges:
      (none)
  Member "an area and energy efficient design of domain wall memory based deep convolutional neural networks using stochastic computing":
    also connected through other hyperedges:
      (none)
  Member "adam admm a unified systematic framework of structured weight pruning for dnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a privacy preserving dnn pruning and mobile acceleration framework")
  Member "scalable noc based neuromorphic hardware learning and inference":
    also connected through other hyperedges:
      (none)
  Member "group scissor scaling neuromorphic computing design to large neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array")

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)