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: "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
  Abstract: The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks. However, their practical applications are often restricted in resource-constrained environments. In this paper, we introduce projection convolutional neural networks (PCNNs) with a discrete back propagation via projection (DBPP) to improve the performance of binarized neural networks (BNNs). The contributions of our paper include: 1) for the first time, the projection function is exploited to efficiently solve the discrete back propagation problem, which leads to a new highly compressed CNNs (termed PCNNs); 2) by exploiting multiple projections, we learn a set of diverse quantized kernels that compress the full-precision kernels in a more efficient way than those proposed previously; 3) PCNNs achieve the best classification performance compared to other state-of-the-art BNNs on the ImageNet and CIFAR datasets.

Connected through hyperedge HE-1 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"

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"
    - "training and inference with integers in deep neural networks"

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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"

Connected through hyperedge HE-4 (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"
    - "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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "binarized neural architecture search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dynamic distribution pruning for efficient network architecture search"
    - "multinomial distribution learning for effective neural architecture search"
    - "searching for accurate binary neural architectures"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "balanced binary neural networks with gated residual"):
  with members:
    - "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"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "searching for accurate binary neural architectures"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"

Connected through hyperedge HE-7 (induced by source paper "bayesian optimized 1 bit cnns"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "min entropy latent model for weakly supervised object detection"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "training and inference with integers in deep neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "towards accurate binary convolutional neural network"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "searching for accurate binary neural architectures"):
  with members:
    - "attribute aware pooling for pedestrian attribute recognition"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "regularized evolution for image classifier architecture search"
    - "wrpn wide reduced precision networks"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "autoencoder inspired unsupervised feature selection"

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)