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: "ristretto hardware oriented approximation of convolutional neural networks"
  Abstract: Convolutional neural networks (CNN) have achieved major breakthroughs in recent years. Their performance in computer vision have matched and in some areas even surpassed human capabilities. Deep neural networks can capture complex non-linear features; however this ability comes at the cost of high computational and memory requirements. State-of-art networks require billions of arithmetic operations and millions of parameters. To enable embedded devices such as smartphones, Google glasses and monitoring cameras with the astonishing power of deep learning, dedicated hardware accelerators can be used to decrease both execution time and power consumption. In applications where fast connection to the cloud is not guaranteed or where privacy is important, computation needs to be done locally. Many hardware accelerators for deep neural networks have been proposed recently. A first important step of accelerator design is hardware-oriented approximation of deep networks, which enables energy-efficient inference. We present Ristretto, a fast and automated framework for CNN approximation. Ristretto simulates the hardware arithmetic of a custom hardware accelerator. The framework reduces the bit-width of network parameters and outputs of resource-intense layers, which reduces the chip area for multiplication units significantly. Alternatively, Ristretto can remove the need for multipliers altogether, resulting in an adder-only arithmetic. The tool fine-tunes trimmed networks to achieve high classification accuracy. Since training of deep neural networks can be time-consuming, Ristretto uses highly optimized routines which run on the GPU. This enables fast compression of any given network. Given a maximum tolerance of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.

Connected through hyperedge HE-1 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  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"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-2 (induced by source paper "full deep neural network training on a pruned weight budget"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "revisiting small batch training for deep neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "domain adaptive deep network compression"
    - "training quantized nets a deeper understanding"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-3 (induced by source paper "gdrq group based distribution reshaping for quantization"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "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"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-4 (induced by source paper "semi relaxed quantization with dropbits training low bit neural networks via bit wise regularization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "differentiable fine grained quantization for deep neural network compression"
    - "relaxed quantization for discretized neural networks"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "heterogeneous bitwidth binarization in convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-5 (induced by source paper "deep learning on mobile devices a review"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the limits and potentials of deep learning for robotics"
    - "ai benchmark running deep neural networks on android smartphones"
    - "transfer learning for time series classification"
    - "an analysis of deep neural network models for practical applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "toward robustness against label noise in training deep discriminative neural networks"

Connected through hyperedge HE-6 (induced by source paper "optimally scheduling cnn convolutions for efficient memory access"):
  with members:
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "a systematic approach to blocking convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "performance efficiency trade off of low precision numerical formats in deep neural networks"):
  with members:
    - "deep positron a deep neural network using the posit number system"
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "deep learning inference on embedded devices fixed point vs posit"
    - "wrpn apprentice methods for training and inference using low precision numerics"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "exploring bit slice sparsity in deep neural networks for efficient reram based deployment"):
  with members:
    - "mixed signal charge domain acceleration of deep neural networks through interleaved bit partitioned arithmetic"
    - "model compression via distillation and quantization"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)