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: "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
  Abstract: In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource-limited mobile systems. As the countermeasure to this problem, deep neural networks with ternarized weights (i.e. -1, 0, +1) have been widely explored to greatly reduce the model size and computational cost, with limited accuracy degradation. In this work, we propose a novel ternarized neural network training method which simultaneously optimizes both weights and quantizer during training, differentiating from prior works. Instead of fixed and uniform weight ternarization, we are the first to incorporate the thresholds of weight ternarization into a closed-form representation using the truncated Gaussian approximation, enabling simultaneous optimization of weights and quantizer through back-propagation training. With both of the first and last layer ternarized, the experiments on the ImageNet classification task show that our ternarized ResNet-18/34/50 only has 3.9/2.52/2.16% accuracy degradation in comparison to the full-precision counterparts.

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 "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "adversarial robustness vs model compression or both"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"

Connected through hyperedge HE-3 (induced by source paper "towards unified int8 training for convolutional neural network"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "learned step size quantization"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep mutual learning"

Connected through hyperedge HE-5 (induced by source paper "ir net forward and backward information retention for highly accurate binary neural networks"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "regularizing activation distribution for training binarized deep networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "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"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"

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)