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: "taking human out of learning applications a survey on automated machine learning"
  Abstract: Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "efficient backbone search for scene text recognition"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "detnas backbone search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "benchmark and survey of automated machine learning frameworks"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning in practice state of the art and recent results"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "automl a survey of the state of the art"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"

Connected through hyperedge HE-5 (induced by source paper "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "automl a survey of the state of the art"
    - "survey on automated machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "optimized cost per click in taobao display advertising"
    - "deep cross network for ad click predictions"
    - "one button machine for automating feature engineering in relational databases"

Connected through hyperedge HE-6 (induced by source paper "deep neural networks for marine debris detection in sonar images"):
  with members:
    - "on the convergence of adam and beyond"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "learning transferable architectures for scalable image recognition"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "efficient neural architecture search via proximal iterations"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "bayesnas a bayesian approach for neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "proxquant quantized neural networks via proximal operators"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "how to manipulate cnns to make them lie the gradcam case"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "sok security and privacy in machine learning"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

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.AI(Artificial Intelligence)