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: "mixed precision training of convolutional neural networks using integer operations"
  Abstract: The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on the ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half-precision

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "scalable methods for 8 bit training of neural networks"
    - "nvidia tensor core programmability performance precision"
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "a survey on methods and theories of quantized neural networks"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-3 (induced by source paper "towards unified int8 training for convolutional neural network"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "learned step size quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-5 (induced by source paper "adaptive precision training quantify back propagation in neural networks with fixed point numbers"):
  with members:
    - "differentiable quantization of deep neural networks"
    - "scalable methods for 8 bit training of neural networks"
    - "a quantization friendly separable convolution for mobilenets"
    - "training and inference with integers in deep neural networks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "training deep neural networks with 8 bit floating point numbers"
    - "haq hardware aware automated quantization with mixed precision"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"

Connected through hyperedge HE-6 (induced by source paper "swalp stochastic weight averaging in low precision training"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "training deep neural networks with 8 bit floating point numbers"
    - "averaging weights leads to wider optima and better generalization"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "training quantized nets a deeper understanding"
    - "computation error analysis of block floating point arithmetic oriented convolution neural network accelerator design"

Connected through hyperedge HE-7 (induced by source paper "a study of bfloat16 for deep learning training"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "deep cross network for ad click predictions"
    - "mixed precision training"
    - "ternary neural networks with fine grained quantization"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "training high performance and large scale deep neural networks with full 8 bit integers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "training and inference with integers in deep neural networks"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "learning low precision deep neural networks through regularization"
    - "training deep neural networks with 8 bit floating point numbers"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mixed precision training"

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.NE(Neural and Evolutionary Computing)