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: "relaxed quantization for discretized neural networks"
  Abstract: Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.

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"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "propagating asymptotic estimated gradients for low bitwidth quantized neural networks"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "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"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "automatic pruning for quantized neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "discrimination aware channel pruning for deep neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "post training quantization with multiple points mixed precision without mixed precision"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "additive powers of two quantization a non uniform discretization for neural networks"
    - "loss aware post training quantization"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-5 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-6 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "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"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-7 (induced by source paper "training quantized neural networks with the full precision auxiliary module"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "relational knowledge distillation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "self supervised knowledge distillation using singular value decomposition"

Connected through hyperedge HE-8 (induced by source paper "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

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)