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: "neural networks with few multiplications"
  Abstract: For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplications, we investigate an approach to training that eliminates the need for most of these. Our method consists of two parts: First we stochastically binarize weights to convert multiplications involved in computing hidden states to sign changes. Second, while back-propagating error derivatives, in addition to binarizing the weights, we quantize the representations at each layer to convert the remaining multiplications into binary shifts. Experimental results across 3 popular datasets (MNIST, CIFAR10, SVHN) show that this approach not only does not hurt classification performance but can result in even better performance than standard stochastic gradient descent training, paving the way to fast, hardware-friendly training of neural networks.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "quantized neural network inference with precision batching"):
  with members:
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "quantized reinforcement learning quarl"
    - "adaptivfloat a floating point based data type for resilient deep learning inference"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "alternating multi bit quantization for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "pact parameterized clipping activation for quantized neural networks"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-3 (induced by source paper "fully quantized transformer for improved translation"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "weighted transformer network for machine translation"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-4 (induced by source paper "eigendamage structured pruning in the kronecker factored eigenbasis"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"
    - "three mechanisms of weight decay regularization"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-5 (induced by source paper "accurate and compact convolutional neural networks with trained binarization"):
  with members:
    - "from hashing to cnns training binaryweight networks via hashing"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "loss aware weight quantization of deep networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning accurate low bit deep neural networks with stochastic quantization"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-6 (induced by source paper "attention based pruning for shift networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "quantized guided pruning for efficient hardware implementations of convolutional neural networks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "constructing fast network through deconstruction of convolution"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-7 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-8 (induced by source paper "magnitude and uncertainty pruning criterion for neural networks"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)