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: "practical deep learning architecture optimization"
  Abstract: The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies. We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform man-made architectures and our competitors which consider the same types of layers.

Connected through hyperedge HE-1 (induced by source paper "dsnas direct neural architecture search without parameter retraining"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "detnas backbone search for object detection"
    - "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"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "neunets an automated synthesis engine for neural network design"):
  with members:
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "neural network acceptability judgments"

Connected through hyperedge HE-3 (induced by source paper "alphax exploring neural architectures with deep neural networks and monte carlo tree search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-4 (induced by source paper "xfernas transfer neural architecture search"):
  with members:
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "tapas train less accuracy predictor for architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"

Connected through hyperedge HE-5 (induced by source paper "refining the structure of neural networks using matrix conditioning"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient forward architecture search"
    - "the singular values of convolutional layers"
    - "trained rank pruning for efficient deep neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-6 (induced by source paper "neural architecture search using deep neural networks and monte carlo tree search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "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)