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: "adaptive quantization for deep neural network"
  Abstract: In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks"):
  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"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-2 (induced by source paper "zeroq a novel zero shot quantization framework"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "hawq v2 hessian aware trace weighted quantization of neural networks"
    - "cat compression aware training for bandwidth reduction"
    - "q bert hessian based ultra low precision quantization of bert"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "the knowledge within methods for data free model compression"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "hawq hessian aware quantization of neural networks with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "geometric estimation of multivariate dependency"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "hawq hessian aware quantization of neural networks with mixed precision"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"

Connected through hyperedge HE-5 (induced by source paper "q bert hessian based ultra low precision quantization of bert"):
  with members:
    - "are sixteen heads really better than one"
    - "a tensorized transformer for language modeling"
    - "lightweight and efficient neural natural language processing with quaternion networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-6 (induced by source paper "extreme language model compression with optimal subwords and shared projections"):
  with members:
    - "q bert hessian based ultra low precision quantization of bert"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "mixed precision quantized neural network with progressively decreasing bitwidth for image classification and object detection"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learned step size quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "training competitive binary neural networks from scratch"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-8 (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"
    - "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"

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)