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: "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
  Abstract: There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website this https URL. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.

Connected through hyperedge HE-1 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "universal adversarial audio perturbations"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "bayesian tensorized neural networks with automatic rank selection"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "gradient descent based d optimal design for the least squares polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "comprehensive snn compression using admm optimization and activity regularization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "training and inference with integers in deep neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"

Connected through hyperedge HE-3 (induced by source paper "self binarizing networks"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "deep learning towards mobile applications"
    - "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"
    - "hashnet deep learning to hash by continuation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-4 (induced by source paper "l1 norm batch normalization for efficient training of deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "benchip benchmarking intelligence processors"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "aggregated residual transformations for deep neural networks"
    - "stacked generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "training high performance and large scale deep neural networks with full 8 bit integers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "learning low precision deep neural networks through regularization"
    - "training deep neural networks with 8 bit floating point numbers"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"

Connected through hyperedge HE-6 (induced by source paper "rtn reparameterized ternary network"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "pact parameterized clipping activation for quantized neural networks"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-7 (induced by source paper "additive noise annealing and approximation properties of quantized neural networks"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "training compact neural networks with binary weights and low precision activations"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "neural networks weights quantization target none retraining ternary tnt"):
  with members:
    - "ternary neural networks with fine grained quantization"
    - "ternary weight networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "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)