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: "wrpn wide reduced precision networks"
  Abstract: For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce this large memory footprint is to reduce the precision of activations. However, past works have shown that reducing the precision of activations hurts model accuracy. We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy. We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory bandwidth and computational energy) and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN - wide reduced-precision networks. We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.

Connected through hyperedge HE-1 (induced by source paper "least squares binary quantization of neural networks"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  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"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"

Connected through hyperedge HE-3 (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-4 (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"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "meliusnet can binary neural networks achieve mobilenet level accuracy"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "bayesian optimized 1 bit cnns"
    - "on the variance of the adaptive learning rate and beyond"
    - "searching for accurate binary neural architectures"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "training competitive binary neural networks from scratch"

Connected through hyperedge HE-6 (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-7 (induced by source paper "precision gating improving neural network efficiency with dynamic dual precision activations"):
  with members:
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "gradient based deep quantization of neural networks through sinusoidal adaptive regularization"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "mixed precision dnns all you need is a good parametrization"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "learned step size quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "loss aware weight quantization of deep networks"

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)