human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "training deep convolutional neural networks with resistive cross point devices"
  Abstract: In a previous work we have detailed the requirements to obtain a maximal performance benefit by implementing fully connected deep neural networks (DNN) in form of arrays of resistive devices for deep learning. This concept of Resistive Processing Unit (RPU) devices we extend here towards convolutional neural networks (CNNs). We show how to map the convolutional layers to RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed due to analog nature of the computations performed on the arrays effect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of RPU approach for large class of neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "txsim modeling training of deep neural networks on resistive crossbar systems"):
  with members:
    - "training large scale anns on simulated resistive crossbar arrays"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "in datacenter performance analysis of a tensor processing unit"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "deep learning with limited numerical precision"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "hardware and software co optimization for the initialization failure of the reram based cross bar array"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "pact parameterized clipping activation for quantized neural networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "identity mappings in deep residual networks"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "neuromorphic computing with multi memristive synapses"
    - "implementing bayesian networks with embedded stochastic mram"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-5 (induced by source paper "a back end cmos compatible ferroelectric field effect transistor for synaptic weights"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"

Connected through hyperedge HE-6 (induced by source paper "accurate deep neural network inference using computational phase change memory"):
  with members:
    - "improving noise tolerance of mixed signal neural networks"
    - "dissecting the nvidia turing t4 gpu via microbenchmarking"
    - "mixed precision in memory computing"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "in datacenter performance analysis of a tensor processing unit"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-7 (induced by source paper "algorithm for training neural networks on resistive device arrays"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "a network of deep neural networks for distant speech recognition"
    - "dual path networks"
    - "exploring the limits of language modeling"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-8 (induced by source paper "training large scale anns on simulated resistive crossbar arrays"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "efficient convnets for analog arrays"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "caffe convolutional architecture for fast feature embedding"

Question: Please tell me which class the center node 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.LG(Machine Learning)