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: "deep convolutional neural network inference with floating point weights and fixed point activations"
  Abstract: Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations. However, the minimum required data precision of fixed-point weights varies across different networks and also across different layers of the same network. In this work, we propose using floating-point numbers for representing the weights and fixed-point numbers for representing the activations. We show that using floating-point representation for weights is more efficient than fixed-point representation for the same bit-width and demonstrate it on popular large-scale CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such a representation scheme enables compact hardware multiply-and-accumulate (MAC) unit design. Experimental results show that the proposed scheme reduces the weight storage by up to 36% and power consumption of the hardware multiplier by up to 50%.

Connected through hyperedge HE-1 (induced by source paper "cloudifiernet deep vision models for artificial image processing"):
  with members:
    - "focal loss for dense object detection"
    - "pix2code generating code from a graphical user interface screenshot"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "efficient stochastic inference of bitwise deep neural networks"
    - "wide residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-2 (induced by source paper "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"):
  with members:
    - "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"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "oriented response networks"
    - "towards accurate binary convolutional neural network"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "wide residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "bi real net binarizing deep network towards real network performance"):
  with members:
    - "uniq uniform noise injection for the quantization of neural networks"
    - "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"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "quenn quantization engine for low power neural networks"):
  with members:
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "imagenet pre trained models with batch normalization"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-5 (induced by source paper "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"):
  with members:
    - "towards accurate binary convolutional neural network"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "end to end active object tracking via reinforcement learning"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"
    - "visual translation embedding network for visual relation detection"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-6 (induced by source paper "effnet an efficient structure for convolutional neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large scale evolution of image classifiers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "noiseout a simple way to prune neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "rethinking machine learning development and deployment for edge devices"):
  with members:
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "not all ops are created equal"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "in datacenter performance analysis of a tensor processing unit"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "bit fusion bit level dynamically composable architecture for accelerating deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-8 (induced by source paper "trading off accuracy and energy of deep inference on embedded systems a co design approach"):
  with members:
    - "cost effective active learning for deep image classification"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep reinforcement learning a brief survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "eie efficient inference engine on compressed deep neural network"
    - "xception deep learning with depthwise separable convolutions"
    - "designing energy efficient convolutional neural networks using energy aware pruning"

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.LG(Machine Learning)