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: "bmxnet an open source binary neural network implementation based on mxnet"
  Abstract: Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could significantly improve the efficiency and lower the energy consumption at runtime, which enables the application of state-of-the-art deep learning models on low power devices. BMXNet is an open-source BNN library based on MXNet, which supports both XNOR-Networks and Quantized Neural Networks. The developed BNN layers can be seamlessly applied with other standard library components and work in both GPU and CPU mode. BMXNet is maintained and developed by the multimedia research group at Hasso Plattner Institute and released under Apache license. Extensive experiments validate the efficiency and effectiveness of our implementation. The BMXNet library, several sample projects, and a collection of pre-trained binary deep models are available for download at this https URL

Connected through hyperedge HE-1 (induced by source paper "meliusnet can binary neural networks achieve mobilenet level accuracy"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "bayesian optimized 1 bit cnns"
    - "on the variance of the adaptive learning rate and beyond"
    - "searching for accurate binary neural architectures"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "training competitive binary neural networks from scratch"

Connected through hyperedge HE-2 (induced by source paper "towards explainable bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "deep learning with limited numerical precision"
    - "low precision storage for deep learning"

Connected through hyperedge HE-3 (induced by source paper "back to simplicity how to train accurate bnns from scratch"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "learning to train a binary neural network"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "bi real net binarizing deep network towards real network performance"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "phonebit efficient gpu accelerated binary neural network inference engine for mobile phones"):
  with members:
    - "hg caffe mobile and embedded neural network gpu opencl inference engine with fp16 supporting"
    - "ai benchmark running deep neural networks on android smartphones"
    - "learning efficient convolutional networks through network slimming"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "dabnn a super fast inference framework for binary neural networks on arm devices"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "a first look at deep learning apps on smartphones"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "structured binary neural networks for accurate image classification and semantic segmentation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "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"
    - "discrimination aware channel pruning for deep neural networks"
    - "learning to train a binary neural network"
    - "syq learning symmetric quantization for efficient deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "training competitive binary neural networks from scratch"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "adaptive quantization for deep neural network"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning to train a binary neural network"):
  with members:
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "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.LG(Machine Learning)