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: "differentiable quantization of deep neural networks"
  Abstract: We propose differentiable quantization (DQ) for efficient deep neural network (DNN) inference where gradient descent is used to learn the quantizer's step size, dynamic range and bitwidth. Training with differentiable quantizers brings two main benefits: first, DQ does not introduce hyperparameters; second, we can learn for each layer a different step size, dynamic range and bitwidth. Our experiments show that DNNs with heterogeneous and learned bitwidth yield better performance than DNNs with a homogeneous one. Further, we show that there is one natural DQ parametrization especially well suited for training. We confirm our findings with experiments on CIFAR-10 and ImageNet and we obtain quantized DNNs with learned quantization parameters achieving state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "bitpruning learning bitlengths for aggressive and accurate quantization"):
  with members:
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "dpred making typical activation values matter in deep learning computing"
    - "dynamic stripes exploiting the dynamic precision requirements of activation values in neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "reduced precision strategies for bounded memory in deep neural nets"

Connected through hyperedge HE-2 (induced by source paper "towards efficient training for neural network quantization"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "a mean field theory of batch normalization"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "fixup initialization residual learning without normalization"
    - "learned step size quantization"
    - "binary ensemble neural network more bits per network or more networks per bit"

Connected through hyperedge HE-3 (induced by source paper "qkd quantization aware knowledge distillation"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "feature fusion for online mutual knowledge distillation"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learned step size quantization"

Connected through hyperedge HE-4 (induced by source paper "adabits neural network quantization with adaptive bit widths"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "towards efficient training for neural network quantization"

Connected through hyperedge HE-5 (induced by source paper "adaptive precision training quantify back propagation in neural networks with fixed point numbers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "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 "training dnn iot applications for deployment on analog nvm crossbars"):
  with members:
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "haq hardware aware automated quantization with mixed precision"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"

Connected through hyperedge HE-7 (induced by source paper "iteratively training look up tables for network quantization"):
  with members:
    - "searching for mobilenetv3"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learned step size quantization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "wrpn wide reduced precision networks"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"

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)