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: "understanding the impact of precision quantization on the accuracy and energy of neural networks"
  Abstract: Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in power budgets dedicated to these networks, the importance of low-power, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work, we address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, powers of two, and binary). In our evaluation, we consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, power and energy consumption, and design area. We also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition, we propose that a small portion of the benefits achieved when using lower precisions can be forfeited to increase the network size and therefore the accuracy. We evaluate our experiments, using three well-recognized networks and datasets to show its generality. We investigate the trade-offs and highlight the benefits of using lower precisions in terms of energy and memory footprint.

Connected through hyperedge HE-1 (induced by source paper "alert accurate anytime learning for energy and timeliness"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "sparsely aggregated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "anytime stereo image depth estimation on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-2 (induced by source paper "efficient inference of cnns via channel pruning"):
  with members:
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "image captioning with semantic attention"

Connected through hyperedge HE-3 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-4 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "a resource efficient embedded iris recognition system using fully convolutional networks"):
  with members:
    - "hardware software codesign of accurate multiplier free deep neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "striving for simplicity the all convolutional net"
    - "training deep neural networks with low precision multiplications"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "optimizing convolutional neural networks for embedded systems by means of neuroevolution"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-8 (induced by source paper "synergy an hw sw framework for high throughput cnns on embedded heterogeneous soc"):
  with members:
    - "fpga based cnn inference accelerator synthesized from multi threaded c software"
    - "finn a framework for fast scalable binarized neural network inference"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "caffe convolutional architecture for fast feature embedding"

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.NE(Neural and Evolutionary Computing)