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: "convolutional neural networks using logarithmic data representation"
  Abstract: Recent advances in convolutional neural networks have considered model complexity and hardware efficiency to enable deployment onto embedded systems and mobile devices. For example, it is now well-known that the arithmetic operations of deep networks can be encoded down to 8-bit fixed-point without significant deterioration in performance. However, further reduction in precision down to as low as 3-bit fixed-point results in significant losses in performance. In this paper we propose a new data representation that enables state-of-the-art networks to be encoded to 3 bits with negligible loss in classification performance. To perform this, we take advantage of the fact that the weights and activations in a trained network naturally have non-uniform distributions. Using non-uniform, base-2 logarithmic representation to encode weights, communicate activations, and perform dot-products enables networks to 1) achieve higher classification accuracies than fixed-point at the same resolution and 2) eliminate bulky digital multipliers. Finally, we propose an end-to-end training procedure that uses log representation at 5-bits, which achieves higher final test accuracy than linear at 5-bits.

Connected through hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks"):
  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"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-2 (induced by source paper "switchable precision neural networks"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"

Connected through hyperedge HE-3 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks"):
  with members:
    - "weight normalization based quantization for deep neural network compression"
    - "knowledge distillation for optimization of quantized deep neural networks"
    - "quantization networks"
    - "swalp stochastic weight averaging in low precision training"
    - "empirical analysis of knowledge distillation technique for optimization of quantized deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "additive powers of two quantization an efficient non uniform discretization for neural networks"):
  with members:
    - "weight normalization based quantization for deep neural network compression"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "learned step size quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic neural network compression by sparsity quantization joint learning a constrained optimization based approach"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "ecc energy constrained deep neural network compression via a bilinear regression model"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "hawq hessian aware quantization of neural networks with mixed precision"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"

Connected through hyperedge HE-7 (induced by source paper "differentiable soft quantization bridging full precision and low bit neural networks"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "releq an automatic reinforcement learning approach for deep quantization of neural networks"

Connected through hyperedge HE-8 (induced by source paper "data free quantization through weight equalization and bias correction"):
  with members:
    - "equi normalization of neural networks"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "probabilistic binary neural networks"
    - "relaxed quantization for discretized neural networks"

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.NE(Neural and Evolutionary Computing)