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: "one shot neural architecture search via self evaluated template network"
  Abstract: Neural architecture search (NAS) aims to automate the search procedure of architecture instead of manual design. Even if recent NAS approaches finish the search within days, lengthy training is still required for a specific architecture candidate to get the parameters for its accurate evaluation. Recently one-shot NAS methods are proposed to largely squeeze the tedious training process by sharing parameters across candidates. In this way, the parameters for each candidate can be directly extracted from the shared parameters instead of training them from scratch. However, they have no sense of which candidate will perform better until evaluation so that the candidates to evaluate are randomly sampled and the top-1 candidate is considered the best. In this paper, we propose a Self-Evaluated Template Network (SETN) to improve the quality of the architecture candidates for evaluation so that it is more likely to cover competitive candidates. SETN consists of two components: (1) an evaluator, which learns to indicate the probability of each individual architecture being likely to have a lower validation loss. The candidates for evaluation can thus be selectively sampled according to this evaluator. (2) a template network, which shares parameters among all candidates to amortize the training cost of generated candidates. In experiments, the architecture found by SETN achieves state-of-the-art performance on CIFAR and ImageNet benchmarks within comparable computation costs. Code is publicly available on GitHub: this https URL.

Connected through hyperedge HE-1 (induced by source paper "nas bench 201 extending the scope of reproducible neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"
    - "dropblock a regularization method for convolutional networks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "nas bench 102 extending the scope of reproducible neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"
    - "dropblock a regularization method for convolutional networks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "learning correspondence from the cycle consistency of time"
    - "votenet a deep learning label fusion method for multi atlas segmentation"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "one shot neural architecture search through a posteriori distribution guided sampling"
    - "neural multi atlas label fusion application to cardiac mr images"

Connected through hyperedge HE-4 (induced by source paper "adversarialnas adversarial neural architecture search for gans"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "agan towards automated design of generative adversarial networks"
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "autogan neural architecture search for generative adversarial networks"
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "fair darts eliminating unfair advantages in differentiable architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "xnas neural architecture search with expert advice"
    - "single path one shot neural architecture search with uniform sampling"

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)