human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient differentiable neural architecture search with meta kernels"):
  Abstract: The searching procedure of neural architecture search (NAS) is notoriously time consuming and cost this http URL make the search space continuous, most existing gradient-based NAS methods relax the categorical choice of a particular operation to a softmax over all possible operations and calculate the weighted sum of multiple features, resulting in a large memory requirement and a huge computation burden. In this work, we propose an efficient and novel search strategy with meta kernels. We directly encode the supernet from the perspective on convolution kernels and "shrink" multiple convolution kernel candidates into a single one before these candidates operate on the input feature. In this way, only a single feature is generated between two intermediate nodes. The memory for storing intermediate features and the resource budget for conducting convolution operations are both reduced remarkably. Despite high efficiency, our search strategy can search in a more fine-grained way than existing works and increases the capacity for representing possible networks. We demonstrate the effectiveness of our search strategy by conducting extensive experiments. Specifically, our method achieves 77.0% top-1 accuracy on ImageNet benchmark dataset with merely 357M FLOPs, outperforming both EfficientNet and MobileNetV3 under the same FLOPs constraints. Compared to models discovered by the start-of-the-art NAS method, our method achieves the same (sometimes even better) performance, while faster by three orders of magnitude.

Includes members:
  Member "res2net a new multi scale backbone architecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "autogan neural architecture search for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural architecture search for deep image prior")
  Member "single path nas designing hardware efficient convnets in less than 4 hours":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "searching for a robust neural architecture in four gpu hours":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning architectures for binary networks")
  Member "detnas backbone search for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nas count counting by density with neural architecture search")
  Member "exploring randomly wired neural networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "densely connected search space for more flexible neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

Question: Please tell me which class the source paper 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)