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 "stochastic sparse learning with momentum adaptation for imprecise memristor networks"):
  Abstract: Memristor based neural networks have great potentials in on-chip neuromorphic computing systems due to the fast computation and low-energy consumption. However, the imprecise properties of existing memristor devices generally result in catastrophic failures for the network in-situ training, which significantly impedes their engineering applications. In this work, we design a novel learning scheme that integrates stochastic sparse updating with momentum adaption (SSM) to efficiently train the imprecise memristor networks with high classification accuracy. The SSM scheme consists of: (1) a stochastic and discrete learning method to make weight updates sparse; (2) a momentum based gradient algorithm to eliminate training noises and distill robust updates; (3) a network re-initialization method to mitigate the device-to-device variation; (4) an update compensation strategy to further stabilize the weight programming process. With the SSM scheme, experiments show that the classification accuracy on multilayer perceptron (MLP) and convolutional neural network (CNN) improves from 26.12% to 90.07% and from 65.98% to 92.38%, respectively. Meanwhile, the total numbers of weight updating pulses decrease 90% and 40% in MLP and CNN, respectively, and the convergence rates are both 3x faster. The SSM scheme provides a high-accuracy, low-power, and fast-convergence solution for the in-situ training of imprecise memristor networks, which is crucial to future neuromorphic intelligence systems.

Includes members:
  Member "training and inference with integers in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "all you need is a good init":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")

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)