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 lstm networks with resistive cross point devices"
  Abstract: In our previous work we have shown that resistive cross point devices, so called Resistive Processing Unit (RPU) devices, can provide significant power and speed benefits when training deep fully connected networks as well as convolutional neural networks. In this work, we further extend the RPU concept for training recurrent neural networks (RNNs) namely LSTMs. We show that the mapping of recurrent layers is very similar to the mapping of fully connected layers and therefore the RPU concept can potentially provide large acceleration factors for RNNs as well. In addition, we study the effect of various device imperfections and system parameters on training performance. Symmetry of updates becomes even more crucial for RNNs; already a few percent asymmetry results in an increase in the test error compared to the ideal case trained with floating point numbers. Furthermore, the input signal resolution to device arrays needs to be at least 7 bits for successful training. However, we show that a stochastic rounding scheme can reduce the input signal resolution back to 5 bits. Further, we find that RPU device variations and hardware noise are enough to mitigate overfitting, so that there is less need for using dropout. We note that the models trained here are roughly 1500 times larger than the fully connected network trained on MNIST dataset in terms of the total number of multiplication and summation operations performed per epoch. Thus, here we attempt to study the validity of the RPU approach for large scale networks.

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"
    - "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"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "hardware and software co optimization for the initialization failure of the reram based cross bar array"):
  with members:
    - "pact parameterized clipping activation for quantized neural networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "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-3 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural ordinary differential equations"):
  with members:
    - "rethinking floating point for deep learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-4 (induced by source paper "algorithm for training neural networks on resistive device arrays"):
  with members:
    - "a network of deep neural networks for distant speech recognition"
    - "dual path networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "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-5 (induced by source paper "training large scale anns on simulated resistive crossbar arrays"):
  with members:
    - "efficient convnets for analog arrays"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "streaming batch eigenupdates for hardware neural networks"):
  with members:
    - "analog cmos based resistive processing unit for deep neural network training"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "the fast convergence of incremental pca"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-7 (induced by source paper "non volatile memory array based quantization and noise resilient lstm neural networks"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "effective quantization approaches for recurrent neural networks"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "design and characterization of superconducting nanowire based processors for acceleration of deep neural network training"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "natural language processing almost from scratch"

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)