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 "accurate deep neural network inference using computational phase change memory"):
  Abstract: In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within memory units by exploiting the physical attributes of memory devices. For instance, crossbar arrays of resistive memory devices can be used to store a matrix and perform analog matrix-vector multiplications at constant O(1) time complexity without intermediate movements of data. This functionality is very appealing for making energy-efficient deep learning inference hardware, where the weights of the neural network layers would be encoded in such crossbar arrays. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in appreciable loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in almost no accuracy loss when transferring weights to analog in-memory computing hardware based on phase-change memory (PCM). Our experimental results demonstrate an as-programmed classification accuracy of 93.69% on the CIFAR-10 dataset with ResNet-32, which stays above 92.6% over a one day period, where each of the 361,722 synaptic weights of the network is programmed on just two PCM devices organized in a differential configuration.

Includes members:
  Member "improving noise tolerance of mixed signal neural networks":
    also connected through other hyperedges:
      (none)
  Member "dissecting the nvidia turing t4 gpu via microbenchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gpu tensor cores for fast arithmetic reductions")
  Member "mixed precision in memory computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "discovering low precision networks close to full precision networks for efficient embedded inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "training deep convolutional neural networks with resistive cross point devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "improved regularization of convolutional neural networks with cutout":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "deep neural networks are robust to weight binarization and other non linear distortions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")

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)