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: "dpp net device aware progressive search for pareto optimal neural architectures"
  Abstract: Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, these techniques typically ignore device-related objectives such as inference time, memory usage, and power consumption. Optimizing neural architecture for device-related objectives is immensely crucial for deploying deep networks on portable devices with limited computing resources. We propose DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e.g., inference time and memory usage) and device-agnostic (e.g., accuracy and model size) objectives. DPP-Net employs a compact search space inspired by current state-of-the-art mobile CNNs, and further improves search efficiency by adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10 are poised to demonstrate the effectiveness of Pareto-optimal networks found by DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2) NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53. Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better performances: higher accuracy and shorter inference time on various devices. Additional experimental results show that models found by DPP-Net also achieve considerably-good performance on ImageNet as well.

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 "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi objective neural architecture search via non stationary policy gradient"):
  with members:
    - "nas evaluation is frustratingly hard"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "ponas progressive one shot neural architecture search for very efficient deployment"):
  with members:
    - "hm nas efficient neural architecture search via hierarchical masking"
    - "efficient differentiable neural architecture search with meta kernels"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "cars continuous evolution for efficient neural architecture search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "progressive darts bridging the optimization gap for nas in the wild"
    - "autogan neural architecture search for generative adversarial networks"
    - "auto reid searching for a part aware convnet for person re identification"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "learning sparse networks using targeted dropout"

Connected through hyperedge HE-6 (induced by source paper "metapruning meta learning for automatic neural network channel pruning"):
  with members:
    - "meta sr a magnification arbitrary network for super resolution"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "metaanchor learning to detect objects with customized anchors"
    - "slimmable neural networks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"

Connected through hyperedge HE-7 (induced by source paper "densely connected search space for more flexible neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "network pruning via transformable architecture search"
    - "vargnet variable group convolutional neural network for efficient embedded computing"
    - "exploring randomly wired neural networks for image recognition"
    - "customizable architecture search for semantic segmentation"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-8 (induced by source paper "automated design of error resilient and hardware efficient deep neural networks"):
  with members:
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "methods for interpreting and understanding deep neural networks"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

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)