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: "reduced precision strategies for bounded memory in deep neural nets"
  Abstract: This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study considers networks where each layer may use different precision data. Our key result is the observation that the tolerance of CNNs to reduced precision data not only varies across networks, a well established observation, but also within networks. Tuning precision per layer is appealing as it could enable energy and performance improvements. In this paper we study how error tolerance across layers varies and propose a method for finding a low precision configuration for a network while maintaining high accuracy. A diverse set of CNNs is analyzed showing that compared to a conventional implementation using a 32-bit floating-point representation for all layers, and with less than 1% loss in relative accuracy, the data footprint required by these networks can be reduced by an average of 74% and up to 92%.

Connected through hyperedge HE-1 (induced by source paper "bitpruning learning bitlengths for aggressive and accurate quantization"):
  with members:
    - "differentiable quantization of deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "dpred making typical activation values matter in deep learning computing"
    - "dynamic stripes exploiting the dynamic precision requirements of activation values in neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "reliable and energy efficient mlc stt ram buffer for cnn accelerators"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "a simple method to reduce off chip memory accesses on convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "convolutional neural network quantization using generalized gamma distribution"
    - "mnasnet platform aware neural architecture search for mobile"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-4 (induced by source paper "dpred making typical activation and weight values matter in deep learning computing"):
  with members:
    - "value aware quantization for training and inference of neural networks"
    - "dynamic stripes exploiting the dynamic precision requirements of activation values in neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning deep cnn denoiser prior for image restoration"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "quenn quantization engine for low power neural networks"):
  with members:
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep learning inference on embedded devices fixed point vs posit"):
  with members:
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "ternary weight networks"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-7 (induced by source paper "convolutional neural network quantization using generalized gamma distribution"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "hardware oriented approximation of convolutional neural networks"
    - "dsd dense sparse dense training for deep neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-8 (induced by source paper "deploying customized data representation and approximate computing in machine learning applications"):
  with members:
    - "a hardware friendly algorithm for scalable training and deployment of dimensionality reduction models on fpga"
    - "fft based deep learning deployment in embedded systems"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "high performance fpga implementation of equivariant adaptive separation via independence algorithm for independent component analysis"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural 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.LG(Machine Learning)