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: "uniq uniform noise injection for the quantization of neural networks"
  Abstract: We present a novel method for training deep neural network amenable to inference in low-precision arithmetic with quantized weights and activations. The training is performed in full precision with random noise injection emulating quantization noise. In order to circumvent the need to simulate realistic quantization noise distributions, the weight and the activation distributions are uniformized by a non-linear transformation, and uniform noise is injected. An inverse transformation is then applied. This procedure emulates a non-uniform k-quantile quantizer at inference time, which is shown to achieve state-of-the-art results for training low-precision networks on CIFAR-10 and ImageNet-1K datasets. In particular, we observe no degradation in accuracy for MobileNet and ResNet-18 on ImageNet with as low as 2-bit quantization of the activations and minimal degradation for as little as 4 bits for the weights.

Connected through hyperedge HE-1 (induced by source paper "feature map transform coding for energy efficient cnn inference"):
  with members:
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "regularizing activation distribution for training binarized deep networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "swalp stochastic weight averaging in low precision training"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "faster gaze prediction with dense networks and fisher pruning"

Connected through hyperedge HE-2 (induced by source paper "fighting quantization bias with bias"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "a quantization friendly separable convolution for mobilenets"
    - "model compression via distillation and quantization"
    - "quantizing convolutional neural networks for low power high throughput inference engines"

Connected through hyperedge HE-3 (induced by source paper "scalable model compression by entropy penalized reparameterization"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "relaxed quantization for discretized neural networks"
    - "entropy constrained training of deep neural networks"
    - "coreset based neural network compression"
    - "end to end optimized image compression"
    - "ternary weight networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "bi real net binarizing deep network towards real network performance"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "discovering low precision networks close to full precision networks for efficient embedded inference"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "high accuracy low precision training"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "averaging weights leads to wider optima and better generalization"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-6 (induced by source paper "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "optimize deep convolutional neural network with ternarized weights and high accuracy"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "towards accurate binary convolutional neural network"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-7 (induced by source paper "efficient non uniform quantizer for quantized neural network targeting reconfigurable hardware"):
  with members:
    - "streaming architecture for large scale quantized neural networks on an fpga based dataflow platform"
    - "model compression via distillation and quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "in datacenter performance analysis of a tensor processing unit"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "finn a framework for fast scalable binarized neural network inference"

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)