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: "modeling the resource requirements of convolutional neural networks on mobile devices"
  Abstract: Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, profiling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how efficiently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome profiling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations.

Connected through hyperedge HE-1 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "performance analysis and characterization of training deep learning models on mobile devices"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "beyond data and model parallelism for deep neural networks"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"

Connected through hyperedge HE-3 (induced by source paper "performance analysis and characterization of training deep learning models on nvidia tx2"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "beyond data and model parallelism for deep neural networks"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-4 (induced by source paper "high throughput cnn inference on embedded arm big little multi core processors"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "tvm end to end optimization stack for deep learning"
    - "ai benchmark running deep neural networks on android smartphones"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "scheduling computation graphs of deep learning models on manycore cpus"
    - "optimizing cnn model inference on cpus"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

Connected through hyperedge HE-5 (induced by source paper "channel gating neural networks"):
  with members:
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "dynamic channel pruning feature boosting and suppression"
    - "learning efficient convolutional networks through network slimming"
    - "an analysis of deep neural network models for practical applications"
    - "towards accurate binary convolutional neural network"
    - "blockdrop dynamic inference paths in residual networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "a berkeley view of systems challenges for ai"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "structured pruning of deep convolutional neural networks"
    - "rethinking the inception architecture for computer vision"

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)