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: "a survey on neural architecture search"
  Abstract: The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning techniques are computationally intensive and their application requires a high level of domain knowledge. Therefore, even partial automation of this process helps to make deep learning more accessible to both researchers and practitioners. With this survey, we provide a formalism which unifies and categorizes the landscape of existing methods along with a detailed analysis that compares and contrasts the different approaches. We achieve this via a comprehensive discussion of the commonly adopted architecture search spaces and architecture optimization algorithms based on principles of reinforcement learning and evolutionary algorithms along with approaches that incorporate surrogate and one-shot models. Additionally, we address the new research directions which include constrained and multi-objective architecture search as well as automated data augmentation, optimizer and activation function search.

Connected through hyperedge HE-1 (induced by source paper "to share or not to share a comprehensive appraisal of weight sharing"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "deeper insights into weight sharing in neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "understanding and improving one shot neural architecture optimization"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "evolutionary optimization of deep learning activation functions"):
  with members:
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "large scale evolution of image classifiers"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "swish a self gated activation function"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "cifar 10 image classification using feature ensembles"):
  with members:
    - "ensembles of feedforward designed convolutional neural networks"
    - "xnas neural architecture search with expert advice"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "inferring convolutional neural networks accuracies from their architectural characterizations"):
  with members:
    - "leanresnet a low cost yet effective convolutional residual networks"
    - "deep learning for vertex reconstruction of neutrino nucleus interaction events with combined energy and time data"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "nasib neural architecture search within budget"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "instanas instance aware neural architecture search"
    - "neural architecture construction using envelopenets"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

Connected through hyperedge HE-6 (induced by source paper "rapdarts resource aware progressive differentiable architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "regularized evolution for image classifier architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "constrained deep neural network architecture search for iot devices accounting hardware calibration"):
  with members:
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "tapas train less accuracy predictor for architecture search"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "sub architecture ensemble pruning in neural architecture search"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "blockqnn efficient block wise neural network architecture generation"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"

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)