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: "resource efficient neural architect"
  Abstract: Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy but lacked consideration of computational resource use. Presented herein are embodiments of a Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA embodiments use a policy network to process the network embeddings to generate new configurations. Example demonstrates of RENA embodiments on image recognition and keyword spotting (KWS) problems are also presented herein. RENA embodiments can find novel architectures that achieve high performance even with tight resource constraints. For the CIFAR10 dataset, the tested embodiment achieved 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size was less than 3M parameters. For the Google Speech Commands Dataset, the tested RENA embodiment achieved the state-of-the-art accuracy without resource constraints, and it outperformed the optimized architectures with tight resource constraints.

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 "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-4 (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"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-5 (induced by source paper "single path mobile automl efficient convnet design and nas hyperparameter optimization"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-6 (induced by source paper "learnable embedding space for efficient neural architecture compression"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

Connected through hyperedge HE-7 (induced by source paper "rc darts resource constrained differentiable architecture search"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "accelerating neural architecture search using performance prediction"

Connected through hyperedge HE-8 (induced by source paper "inductive transfer for neural architecture optimization"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "hierarchical representations for efficient 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.NE(Neural and Evolutionary Computing)