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: "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
  Abstract: Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices given their high computational and memory requirements. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage. In particular, we propose a new balanced metric called NetScore, which is designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network, which is important for on-device edge operation. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. The proposed NetScore metric, along with the other tested metrics, are by no means perfect, but the hope is to push the conversation towards better universal metrics for evaluating deep neural networks for use in practical on-device edge scenarios to help guide practitioners in model design for such scenarios.

Connected through hyperedge HE-1 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "mnasnet platform aware neural architecture search for mobile"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-2 (induced by source paper "edgesegnet a compact network for semantic segmentation"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "yolo nano a highly compact you only look once convolutional neural network for object detection"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "mnasnet platform aware neural architecture search for mobile"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "ferminets learning generative machines to generate efficient neural networks via generative synthesis"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "projectionnet learning efficient on device deep networks using neural projections"

Connected through hyperedge HE-5 (induced by source paper "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"):
  with members:
    - "towards principled design of deep convolutional networks introducing simpnet"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)