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: "you only search once single shot neural architecture search via direct sparse optimization"
  Abstract: Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning view to NAS problem. In specific, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, we impose sparse regularizations to prune useless connections in the architecture. Lastly, we derive an efficient and theoretically sound optimization method to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore can be directly applied to large datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an average test error 2.84\%, while on the ImageNet dataset DSO-NAS achieves 25.4\% test error under 600M FLOPs with 8 GPUs in 18 hours.

Connected through hyperedge HE-1 (induced by source paper "deeper insights into weight sharing in neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "weight agnostic neural networks"
    - "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"
    - "do better imagenet models transfer better"
    - "evolutionary architecture search for deep multitask networks"

Connected through hyperedge HE-2 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "on the convergence of adam and beyond"
    - "cinic 10 is not imagenet or cifar 10"

Connected through hyperedge HE-4 (induced by source paper "single path one shot neural architecture search with uniform sampling"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "regularized evolution for image classifier architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "neural architecture refinement a practical way for avoiding overfitting in nas"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evolutionary neural architecture search for image restoration"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "arcface additive angular margin loss for deep face recognition"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"

Connected through hyperedge HE-6 (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"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "efficient progressive neural architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "memnet memory efficiency guided neural architecture search with augment trim learning"):
  with members:
    - "dvolver efficient pareto optimal neural network architecture search"
    - "ktan knowledge transfer adversarial network"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "wearable audio and imu based shot detection in racquet sports"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-8 (induced by source paper "nasib neural architecture search within budget"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "a survey on neural architecture search"
    - "instanas instance aware neural architecture search"
    - "neural architecture construction using envelopenets"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

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)