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: "shiftcnn generalized low precision architecture for inference of convolutional neural networks"
  Abstract: In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and allows to decrease the number of product operations by at least two orders of magnitude. The proposed architecture targets custom inference accelerators and can be realized on FPGAs or ASICs. Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be converted without retraining into ShiftCNN with less than 1% drop in accuracy when the proposed quantization algorithm is employed. RTL simulations, targeting modern FPGAs, show that power consumption of convolutional layers is reduced by a factor of 4 compared to conventional 8-bit fixed-point architectures.

Connected through hyperedge HE-1 (induced by source paper "fully learnable group convolution for acceleration of deep neural networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "learning transferable architectures for scalable image recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "data free quantization through weight equalization and bias correction"):
  with members:
    - "equi normalization of neural networks"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "probabilistic binary neural networks"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-3 (induced by source paper "deepshift towards multiplication less neural networks"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "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"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-4 (induced by source paper "she a fast and accurate privacy preserving deep neural network via leveled tfhe and logarithmic data representation"):
  with members:
    - "low latency privacy preserving inference"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deepsecure scalable provably secure deep learning"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-5 (induced by source paper "she a fast and accurate deep neural network for encrypted data"):
  with members:
    - "low latency privacy preserving inference"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "a system level solution for low power object detection"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "ssd single shot multibox detector"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-8 (induced by source paper "training a binary weight object detector by knowledge transfer for autonomous driving"):
  with members:
    - "performance guaranteed network acceleration via high order residual quantization"
    - "domain adaptive deep network compression"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "ternary neural networks with fine grained 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)