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: "xnas neural architecture search with expert advice"
  Abstract: This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice. Its optimization criterion is well fitted for an architecture-selection, i.e., it minimizes the regret incurred by a sub-optimal selection of operations. Unlike previous search relaxations, that require hard pruning of architectures, our method is designed to dynamically wipe out inferior architectures and enhance superior ones. It achieves an optimal worst-case regret bound and suggests the use of multiple learning-rates, based on the amount of information carried by the backward gradients. Experiments show that our algorithm achieves a strong performance over several image classification datasets. Specifically, it obtains an error rate of 1.6% for CIFAR-10, 24% for ImageNet under mobile settings, and achieves state-of-the-art results on three additional datasets.

Connected through hyperedge HE-1 (induced by source paper "cifar 10 image classification using feature ensembles"):
  with members:
    - "ensembles of feedforward designed convolutional neural networks"
    - "a survey on neural architecture search"
    - "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-2 (induced by source paper "fair darts eliminating unfair advantages in differentiable architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-3 (induced by source paper "stabilizing darts with amended gradient estimation on architectural parameters"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "darts improved differentiable architecture search with early stopping"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "neural input search for large scale recommendation models"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "nas evaluation is frustratingly hard"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "evaluating the search phase of neural architecture search"
    - "manas multi agent neural architecture search"
    - "darts improved differentiable architecture search with early stopping"
    - "stacnas towards stable and consistent differentiable neural architecture search"
    - "best practices for scientific research on neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "improving one shot nas by suppressing the posterior fading"):
  with members:
    - "am lfs automl for loss function search"
    - "online hyper parameter learning for auto augmentation strategy"
    - "mixconv mixed depthwise convolutional kernels"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "understanding neural architecture search techniques"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"

Connected through hyperedge HE-7 (induced by source paper "stacnas towards stable and consistent differentiable neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "understanding and robustifying differentiable architecture search"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "darts improved differentiable architecture search with early stopping"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "understanding architectures learnt by cell based neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "evaluating the search phase of neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "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.LG(Machine Learning)