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: "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
  Abstract: In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision towards a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 27x faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search towards promising neural-network configurations.

Connected through hyperedge HE-1 (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"
    - "practical deep learning architecture optimization"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "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-2 (induced by source paper "constrained deep neural network architecture search for iot devices accounting hardware calibration"):
  with members:
    - "a survey on neural architecture search"
    - "tapas train less accuracy predictor for architecture search"
    - "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-3 (induced by source paper "you might also like this model data driven approach for recommending deep learning models for unknown image datasets"):
  with members:
    - "do better imagenet models transfer better"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "peephole predicting network performance before training"
    - "hierarchical representations for efficient architecture search"

Connected through hyperedge HE-4 (induced by source paper "tapas train less accuracy predictor for architecture search"):
  with members:
    - "incremental training of deep convolutional neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "peephole predicting network performance before training"

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.CV(Computer Vision and Pattern Recognition)