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: "towards the limit of network quantization"
  Abstract: Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks. It reduces the number of distinct network parameter values by quantization in order to save the storage for them. In this paper, we design network quantization schemes that minimize the performance loss due to quantization given a compression ratio constraint. We analyze the quantitative relation of quantization errors to the neural network loss function and identify that the Hessian-weighted distortion measure is locally the right objective function for the optimization of network quantization. As a result, Hessian-weighted k-means clustering is proposed for clustering network parameters to quantize. When optimal variable-length binary codes, e.g., Huffman codes, are employed for further compression, we derive that the network quantization problem can be related to the entropy-constrained scalar quantization (ECSQ) problem in information theory and consequently propose two solutions of ECSQ for network quantization, i.e., uniform quantization and an iterative solution similar to Lloyd's algorithm. Finally, using the simple uniform quantization followed by Huffman coding, we show from our experiments that the compression ratios of 51.25, 22.17 and 40.65 are achievable for LeNet, 32-layer ResNet and AlexNet, respectively.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "kernel quantization for efficient network compression"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "structured multi hashing for model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "searching for mobilenetv3"
    - "model compression by entropy penalized reparameterization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "non discriminative data or weak model on the relative importance of data and model resolution"
    - "measuring the intrinsic dimension of objective landscapes"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-7 (induced by source paper "blockswap fisher guided block substitution for network compression on a budget"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "full deep neural network training on a pruned weight budget"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "neural architecture optimization"

Connected through hyperedge HE-8 (induced by source paper "blockswap fisher guided block substitution for network compression"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "full deep neural network training on a pruned weight budget"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "the lottery ticket hypothesis finding sparse trainable 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.CV(Computer Vision and Pattern Recognition)