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 progressive neural architecture search"
  Abstract: This paper addresses the difficult problem of finding an optimal neural architecture design for a given image classification task. We propose a method that aggregates two main results of the previous state-of-the-art in neural architecture search. These are, appealing to the strong sampling efficiency of a search scheme based on sequential model-based optimization (SMBO), and increasing training efficiency by sharing weights among sampled architectures. Sequential search has previously demonstrated its capabilities to find state-of-the-art neural architectures for image classification. However, its computational cost remains high, even unreachable under modest computational settings. Affording SMBO with weight-sharing alleviates this problem. On the other hand, progressive search with SMBO is inherently greedy, as it leverages a learned surrogate function to predict the validation error of neural architectures. This prediction is directly used to rank the sampled neural architectures. We propose to attenuate the greediness of the original SMBO method by relaxing the role of the surrogate function so it predicts architecture sampling probability instead. We demonstrate with experiments on the CIFAR-10 dataset that our method, denominated Efficient progressive neural architecture search (EPNAS), leads to increased search efficiency, while retaining competitiveness of found architectures.

Connected through hyperedge HE-1 (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-2 (induced by source paper "intra ensemble in neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "fbnet hardware aware efficient convnet design via differentiable 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"
    - "incremental training of deep convolutional neural networks"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "efficient decoupled neural architecture search by structure and operation sampling"):
  with members:
    - "the evolved transformer"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "when nas meets robustness in search of robust architectures against adversarial attacks"):
  with members:
    - "deeper insights into weight sharing in neural architecture search"
    - "intriguing properties of adversarial training"
    - "exploring randomly wired neural networks for image recognition"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "feature denoising for improving adversarial robustness"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "mfas multimodal fusion architecture search"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "centralnet a multilayer approach for multimodal fusion"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "fast and reliable architecture selection for convolutional neural networks"):
  with members:
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "structure optimization for deep multimodal fusion networks using graph induced kernels"

Connected through hyperedge HE-7 (induced by source paper "taking human out of learning applications a survey on automated machine learning"):
  with members:
    - "meta learning a survey"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "transfer learning with neural automl"
    - "accelerating neural architecture search using performance prediction"

Connected through hyperedge HE-8 (induced by source paper "a review of meta reinforcement learning for deep neural networks architecture search"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "efficient architecture search by network transformation"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep reinforcement learning a brief survey"
    - "a genetic programming approach to designing convolutional neural network architectures"

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)