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: "syq learning symmetric quantization for efficient deep neural networks"
  Abstract: Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook. For very low-precisions, such as binary or ternary networks with 1-8-bit activations, the information loss from quantization leads to significant accuracy degradation due to large gradient mismatches between the forward and backward functions. In this paper, we introduce a quantization method to reduce this loss by learning a symmetric codebook for particular weight subgroups. These subgroups are determined based on their locality in the weight matrix, such that the hardware simplicity of the low-precision representations is preserved. Empirically, we show that symmetric quantization can substantially improve accuracy for networks with extremely low-precision weights and activations. We also demonstrate that this representation imposes minimal or no hardware implications to more coarse-grained approaches. Source code is available at this https URL.

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 "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-3 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"
    - "yolov3 an incremental improvement"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "progressive deep neural networks acceleration via soft filter pruning"

Connected through hyperedge HE-4 (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"
    - "relaxed quantization for discretized 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-5 (induced by source paper "learned threshold pruning"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "taxonomy and evaluation of structured compression of convolutional neural networks"
    - "mixconv mixed depthwise convolutional kernels"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-6 (induced by source paper "reactnet towards precise binary neural network with generalized activation functions"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-7 (induced by source paper "kernel quantization for efficient network compression"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "towards the limit of network quantization"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "near lossless post training quantization of deep neural networks via a piecewise linear approximation"):
  with members:
    - "low bit quantization of neural networks for efficient inference"
    - "stochastic activation pruning for robust adversarial defense"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning with low precision by half wave gaussian quantization"
    - "more is less a more complicated network with less inference complexity"
    - "binaryconnect training deep neural networks with binary weights during propagations"

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)