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: "designing adaptive neural networks for energy constrained image classification"
  Abstract: As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., systems of networks with different accuracy and computation characteristics, where a selection scheme adaptively selects the network to be evaluated for each input image. While previous efforts have investigated different network selection schemes, we find that they do not necessarily result in energy savings when deployed on mobile systems. The key limitation of existing methods is that they learn only how data should be processed among the CNNs and not the network architectures, with each network being treated as a blackbox. #R##N#To address this limitation, we pursue a more powerful design paradigm where the architecture settings of the CNNs are treated as hyper-parameters to be globally optimized. We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device. To efficiently solve this problem, we adapt Bayesian optimization to the properties of the design space, reaching near-optimal configurations in few tens of function evaluations. Our method reduces the energy consumed for image classification on a mobile device by up to 6x, compared to the best previously published work that uses CNNs as blackboxes. Finally, we evaluate two image classification practices, i.e., classifying all images locally versus over the cloud under energy and communication 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 "relevant features based auxiliary cells for energy efficient detection of natural errors"):
  with members:
    - "natural and adversarial error detection using invariance to image transformations"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "distance based confidence score for neural network classifiers"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-4 (induced by source paper "single path nas device aware efficient convnet design"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-5 (induced by source paper "hardware aware machine learning modeling and optimization"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "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"
    - "lightnn filling the gap between conventional deep neural networks and binarized networks"

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)