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: "auto keras an efficient neural architecture search system"
  Abstract: Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. Intensive experiments on real-world benchmark datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art methods. Moreover, we build an open-source AutoML system based on our method, namely Auto-Keras. The system runs in parallel on CPU and GPU, with an adaptive search strategy for different GPU memory limits.

Connected through hyperedge HE-1 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "nas bench 201 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "understanding and robustifying differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "efficient sample based neural architecture search with learnable predictor"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "graph hypernetworks for neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "a framework for democratizing ai"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "a generic framework for privacy preserving deep learning"
    - "deep neural decision trees"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "photon a python api for rapid machine learning model development"):
  with members:
    - "efficient neural architecture search via parameter sharing"
    - "neural architecture search with reinforcement learning"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "caffe convolutional architecture for fast feature embedding"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-5 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "evaluating the search phase of neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "auto gnn neural architecture search of graph neural networks"):
  with members:
    - "agan towards automated design of generative adversarial networks"
    - "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"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "fast graph representation learning with pytorch geometric"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier 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)