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: "learning discrete weights using the local reparameterization trick"
  Abstract: Recent breakthroughs in computer vision make use of large deep neural networks, utilizing the substantial speedup offered by GPUs. For applications running on limited hardware, however, high precision real-time processing can still be a challenge. One approach to solving this problem is training networks with binary or ternary weights, thus removing the need to calculate multiplications and significantly reducing memory size. In this work, we introduce LR-nets (Local reparameterization networks), a new method for training neural networks with discrete weights using stochastic parameters. We show how a simple modification to the local reparameterization trick, previously used to train Gaussian distributed weights, enables the training of discrete weights. Using the proposed training we test both binary and ternary models on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-art results on most experiments.

Connected through hyperedge HE-1 (induced by source paper "propagating asymptotic estimated gradients for low bitwidth quantized neural networks"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "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"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-2 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-3 (induced by source paper "training binary neural networks using the bayesian learning rule"):
  with members:
    - "meliusnet can binary neural networks achieve mobilenet level accuracy"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "mirror descent view for neural network quantization"
    - "latent weights do not exist rethinking binarized neural network optimization"
    - "proximal mean field for neural network quantization"
    - "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models"
    - "wrpn wide reduced precision networks"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "and the bit goes down revisiting the quantization of neural networks"):
  with members:
    - "searching for mobilenetv3"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "a survey on methods and theories of quantized neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-5 (induced by source paper "progressive stochastic binarization of deep networks"):
  with members:
    - "deep convolutional sum product networks for probabilistic image representations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "learning to train a binary neural network"
    - "bnn improved binary network training"

Connected through hyperedge HE-6 (induced by source paper "adaptive loss aware quantization for multi bit networks"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "loss aware weight quantization of deep networks"

Connected through hyperedge HE-7 (induced by source paper "weight normalization based quantization for deep neural network compression"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "synaptic strength for convolutional neural network"
    - "extreme network compression via filter group approximation"
    - "heterogeneous bitwidth binarization in convolutional neural networks"
    - "network sketching exploiting binary structure in deep cnns"

Connected through hyperedge HE-8 (induced by source paper "low bit quantization of neural networks for efficient inference"):
  with members:
    - "model compression via distillation and quantization"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "in datacenter performance analysis of a tensor processing unit"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)