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: "hyperpower power and memory constrained hyper parameter optimization for neural networks"
  Abstract: While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challenges to designers and Machine Learning (ML) practitioners, especially when power and memory constraints need to be considered. In this work, we propose HyperPower, a framework that enables efficient Bayesian optimization and random search in the context of power- and memory-constrained hyper-parameter optimization for NNs running on a given hardware platform. HyperPower is the first work (i) to show that power consumption can be used as a low-cost, a priori known constraint, and (ii) to propose predictive models for the power and memory of NNs executing on GPUs. Thanks to HyperPower, the number of function evaluations and the best test error achieved by a constraint-unaware method are reached up to 112.99x and 30.12x faster, respectively, while never considering invalid configurations. HyperPower significantly speeds up the hyper-parameter optimization, achieving up to 57.20x more function evaluations compared to constraint-unaware methods for a given time interval, effectively yielding significant accuracy improvements by up to 67.6%.

Connected through hyperedge HE-1 (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-2 (induced by source paper "scann synthesis of compact and accurate neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "grow and prune compact fast and accurate lstms"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "integrating hardware diversity with neural architecture search for efficient convolutional neural networks"):
  with members:
    - "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"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-4 (induced by source paper "hardware aware one shot neural architecture search in coordinate ascent framework"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "smash one shot model architecture search through hypernetworks"
    - "neural architecture search with reinforcement learning"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-5 (induced by source paper "chamnet towards efficient network design through platform aware model adaptation"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "resource efficient neural architect"
    - "hardware aware machine learning modeling and optimization"
    - "grow and prune compact fast and accurate lstms"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"

Connected through hyperedge HE-6 (induced by source paper "hardware aware machine learning modeling and optimization"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "designing adaptive neural networks for energy constrained image classification"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "mnasnet platform aware neural architecture search for mobile"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-7 (induced by source paper "designing adaptive neural networks for energy constrained image classification"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adaptive neural networks for efficient inference"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

Connected through hyperedge HE-8 (induced by source paper "tea dnn the quest for time energy accuracy co optimized deep neural networks"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "condensenet an efficient densenet using learned group convolutions"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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.LG(Machine Learning)