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: "best practices for scientific research on neural architecture search"
  Abstract: Finding a well-performing architecture is often tedious for both DL practitioners and researchers, leading to tremendous interest in the automation of this task by means of neural architecture search (NAS). Although the community has made major strides in developing better NAS methods, the quality of scientific empirical evaluations in the young field of NAS is still lacking behind that of other areas of machine learning. To address this issue, we describe a set of possible issues and ways to avoid them, leading to the NAS best practices checklist available at this http URL

Connected through hyperedge HE-1 (induced by source paper "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "randomnet towards fully automatic neural architecture design for multimodal learning"):
  with members:
    - "mfas multimodal fusion architecture search"
    - "streaming end to end speech recognition for mobile devices"
    - "centralnet a multilayer approach for multimodal fusion"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "immunecs neural committee search by an artificial immune system"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deepswarm optimising convolutional neural networks using swarm intelligence"
    - "evaluating the search phase of neural architecture search"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "auto keras an efficient neural architecture search system"
    - "darts differentiable architecture search"

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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "understanding and robustifying differentiable architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural 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)