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: "bnn improved binary network training"
  Abstract: The deployment of Deep neural networks (DNN) on edge devices has been difficult because they are resource hungry. Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to $1$-bit. There is however a significant performance gap between BNNs and floating point DNNs. To reduce this gap, We propose an improved binary training method, by introducing a new regularization function that encourages training weights around binary values. In addition, we add trainable scaling factors to our regularization functions. We also introduce an improved approximation of the derivative of the $sign$ activation function in the backward computation. These modifications are based on linear operations that are easily implementable into the binary training framework. We show experimental results on CIFAR-10 obtaining an accuracy of $87.4\%$, on AlexNet and $83.9\%$ with DoReFa network. On ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of $4\%$ and $2\%$ top-$1$ accuracy respectively. In other words, we significantly reduce the gap between BNNs and floating point DNNs.

Connected through hyperedge HE-1 (induced by source paper "forward and backward information retention for accurate binary neural networks"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "libra r cnn towards balanced learning for object detection"
    - "bayesian optimized 1 bit cnns"
    - "regularizing activation distribution for training binarized deep networks"
    - "dynamic curriculum learning for imbalanced data classification"
    - "xnor net improved binary neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "widening and squeezing towards accurate and efficient qnns"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "searching for accurate binary neural architectures"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "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"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"

Connected through hyperedge HE-4 (induced by source paper "scalable approximate inference and some applications"):
  with members:
    - "stein variational inference for discrete distributions"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "deep probabilistic video compression"
    - "a bandit approach to maximum inner product search"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "stein variational policy gradient"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "training progressively binarizing deep networks using fpgas"):
  with members:
    - "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"
    - "self binarizing networks"
    - "towards binary valued gates for robust lstm training"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "towards accurate binary convolutional neural network"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "stein variational inference for discrete distributions"):
  with members:
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "testing ising models"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "on evaluating adversarial robustness"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "efficient two step adversarial defense for deep neural networks"

Connected through hyperedge HE-8 (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"
    - "learning transferable architectures for scalable 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)