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: "faster gaze prediction with dense networks and fisher pruning"
  Abstract: Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition. However, as we show in this paper, these networks are highly overparameterized for the task of fixation prediction. We first present a simple yet principled greedy pruning method which we call Fisher pruning. Through a combination of knowledge distillation and Fisher pruning, we obtain much more runtime-efficient architectures for saliency prediction, achieving a 10x speedup for the same AUC performance as a state of the art network on the CAT2000 dataset. Speeding up single-image gaze prediction is important for many real-world applications, but it is also a crucial step in the development of video saliency models, where the amount of data to be processed is substantially larger.

Connected through hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "adascale towards real time video object detection using adaptive scaling"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"

Connected through hyperedge HE-2 (induced by source paper "comparing rewinding and fine tuning in neural network pruning"):
  with members:
    - "what is the state of neural network pruning"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "sparse networks from scratch faster training without losing performance"
    - "a constructive prediction of the generalization error across scales"
    - "mlperf training benchmark"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-3 (induced by source paper "legr filter pruning via learned global ranking"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "adascale towards real time video object detection using adaptive scaling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "habitat a platform for embodied ai research"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"

Connected through hyperedge HE-4 (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"
    - "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"
    - "darts differentiable architecture search"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "global sparse momentum sgd for pruning very deep neural networks"):
  with members:
    - "metapruning meta learning for automatic neural network channel pruning"
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"

Connected through hyperedge HE-7 (induced by source paper "selective brain damage measuring the disparate impact of model pruning"):
  with members:
    - "natural adversarial examples"
    - "a benchmark for interpretability methods in deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "efficient neural audio synthesis"
    - "metric learning for novelty and anomaly detection"
    - "snip single shot network pruning based on connection sensitivity"

Connected through hyperedge HE-8 (induced by source paper "deep learning at the edge"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"

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)