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: "netadapt platform aware neural network adaptation for mobile applications"
  Abstract: This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct metrics are evaluated using empirical measurements, so that detailed knowledge of the platform and toolchain is not required. NetAdapt automatically and progressively simplifies a pre-trained network until the resource budget is met while maximizing the accuracy. Experiment results show that NetAdapt achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms. For image classification on the ImageNet dataset, NetAdapt achieves up to a 1.7$\times$ speedup in measured inference latency with equal or higher accuracy on MobileNets (V1&V2).

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-4 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "neural architecture search over decentralized data"):
  with members:
    - "towards federated learning at scale system design"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "federated learning for mobile keyboard prediction"
    - "a first look at deep learning apps on smartphones"

Connected through hyperedge HE-6 (induced by source paper "channel pruning via automatic structure search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "play and prune adaptive filter pruning for deep model compression"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "knapsack pruning with inner distillation"):
  with members:
    - "network pruning via transformable architecture search"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "contrastive representation distillation"
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "learning to prune filters in convolutional neural networks"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)