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: "learning to quantize deep networks by optimizing quantization intervals with task loss"
  Abstract: Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (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"
    - "discrimination aware channel pruning for deep neural networks"
    - "relaxed quantization for discretized 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-3 (induced by source paper "efficient bitwidth search for practical mixed precision neural network"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "regularized evolution for image classifier architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"
    - "releq an automatic reinforcement learning approach for deep quantization of neural networks"
    - "haq hardware aware automated quantization with mixed precision"

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 "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"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-6 (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"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-7 (induced by source paper "differentiable soft quantization bridging full precision and low bit neural networks"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "releq an automatic reinforcement learning approach for deep quantization of neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-8 (induced by source paper "training quantized network with auxiliary gradient module"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "joint neural architecture search and quantization"

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.CV(Computer Vision and Pattern Recognition)