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: "sharpdarts faster and more accurate differentiable architecture search"
  Abstract: Neural Architecture Search (NAS) has been a source of dramatic improvements in neural network design, with recent results meeting or exceeding the performance of hand-tuned architectures. However, our understanding of how to represent the search space for neural net architectures and how to search that space efficiently are both still in their infancy. #R##N#We have performed an in-depth analysis to identify limitations in a widely used search space and a recent architecture search method, Differentiable Architecture Search (DARTS). These findings led us to introduce novel network blocks with a more general, balanced, and consistent design; a better-optimized Cosine Power Annealing learning rate schedule; and other improvements. Our resulting sharpDARTS search is 50% faster with a 20-30% relative improvement in final model error on CIFAR-10 when compared to DARTS. Our best single model run has 1.93% (1.98+/-0.07) validation error on CIFAR-10 and 5.5% error (5.8+/-0.3) on the recently released CIFAR-10.1 test set. To our knowledge, both are state of the art for models of similar size. This model also generalizes competitively to ImageNet at 25.1% top-1 (7.8% top-5) error. #R##N#We found improvements for existing search spaces but does DARTS generalize to new domains? We propose Differentiable Hyperparameter Grid Search and the HyperCuboid search space, which are representations designed to leverage DARTS for more general parameter optimization. Here we find that DARTS fails to generalize when compared against a human's one shot choice of models. We look back to the DARTS and sharpDARTS search spaces to understand why, and an ablation study reveals an unusual generalization gap. We finally propose Max-W regularization to solve this problem, which proves significantly better than the handmade design. Code will be made available.

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"
    - "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"
    - "one shot neural architecture search via compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "on the convergence of adam and beyond"
    - "cinic 10 is not imagenet or cifar 10"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "neural architecture search for deep face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "nas evaluation is frustratingly hard"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "xnas neural architecture search with expert advice"
    - "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-5 (induced by source paper "prune and replace nas"):
  with members:
    - "on network design spaces for visual recognition"
    - "nas bench 101 towards reproducible neural architecture search"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "multinomial distribution learning for effective neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "understanding and improving one shot neural architecture optimization"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "the evolved transformer"
    - "single path one shot neural architecture search with uniform sampling"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "single path nas device aware efficient convnet design"):
  with members:
    - "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"
    - "designing adaptive neural networks for energy constrained image classification"

Connected through hyperedge HE-8 (induced by source paper "neural architecture search in embedding space"):
  with members:
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "don t decay the learning rate increase the batch size"
    - "practical block wise neural network architecture generation"
    - "smash one shot model architecture search through hypernetworks"
    - "sgdr stochastic gradient descent with restarts"

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.CV(Computer Vision and Pattern Recognition)