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: "tapas train less accuracy predictor for architecture search"
  Abstract: In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.

Connected through hyperedge HE-1 (induced by source paper "xfernas transfer neural architecture search"):
  with members:
    - "practical deep learning architecture optimization"
    - "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"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"

Connected through hyperedge HE-2 (induced by source paper "rnas architecture ranking for powerful networks"):
  with members:
    - "cars continuous evolution for efficient neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "co evolutionary compression for unpaired image translation"
    - "searching for accurate binary neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "constrained deep neural network architecture search for iot devices accounting hardware calibration"):
  with members:
    - "a survey on neural architecture search"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "fast task aware architecture inference"):
  with members:
    - "deep sets"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"

Connected through hyperedge HE-5 (induced by source paper "deep bo for hyperparameter optimization of deep networks"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "accelerating neural architecture search using performance prediction"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "knowledge matters importance of prior information for optimization"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-6 (induced by source paper "data proxy generation for fast and efficient neural architecture search"):
  with members:
    - "using small proxy datasets to accelerate hyperparameter search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "peephole predicting network performance before training"
    - "smash one shot model architecture search through hypernetworks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "eigen ecologically inspired genetic approach for neural network structure searching"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "an aggressive genetic programming approach for searching neural network structure under computational constraints"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "blockqnn efficient block wise neural network architecture generation"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "eigen ecologically inspired genetic approach for neural network structure searching from scratch"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "an aggressive genetic programming approach for searching neural network structure under computational constraints"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "blockqnn efficient block wise neural network architecture generation"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"

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)