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: "binarized neural networks"
  Abstract: We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on a different framework, namely Torch7 and Theano, where we train BNNs on MNIST, CIFAR-10 and SVHN, and achieve nearly state-of-the-art results. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which might lead to a great increase in power-efficiency. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available.

Connected through hyperedge HE-1 (induced by source paper "direct training for spiking neural networks faster larger better"):
  with members:
    - "deep learning in spiking neural networks"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "bindsnet a machine learning oriented spiking neural networks library in python"

Connected through hyperedge HE-2 (induced by source paper "effective quantization approaches for recurrent neural networks"):
  with members:
    - "low precision rnns quantizing rnns without losing accuracy"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a survey on deep learning in medical image analysis"
    - "effective quantization methods for recurrent neural networks"
    - "ternary weight networks"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "extremely low bit neural network squeeze the last bit out with admm"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "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"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ternary weight networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-4 (induced by source paper "lightnn filling the gap between conventional deep neural networks and binarized networks"):
  with members:
    - "multiplier less artificial neurons exploiting error resiliency for energy efficient neural computing"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "fixed point factorized networks"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "imagenet pre trained models with batch normalization"
    - "bitwise neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"
    - "learning both weights and connections for efficient neural networks"
    - "quantized convolutional neural networks for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "ternary weight networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ssd single shot multibox detector"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"

Connected through hyperedge HE-7 (induced by source paper "effective quantization methods for recurrent neural networks"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "recurrent neural networks with limited numerical precision"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "adam a method for stochastic optimization"

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)