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: "network pruning via transformable architecture search"
  Abstract: Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a network with flexible channel and layer sizes. The number of the channels/layers is learned by minimizing the loss of the pruned networks. The feature map of the pruned network is an aggregation of K feature map fragments (generated by K networks of different sizes), which are sampled based on the probability distribution.The loss can be back-propagated not only to the network weights, but also to the parameterized distribution to explicitly tune the size of the channels/layers. Specifically, we apply channel-wise interpolation to keep the feature map with different channel sizes aligned in the aggregation procedure. The maximum probability for the size in each distribution serves as the width and depth of the pruned network, whose parameters are learned by knowledge transfer, e.g., knowledge distillation, from the original networks. Experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate the effectiveness of our new perspective of network pruning compared to traditional network pruning algorithms. Various searching and knowledge transfer approaches are conducted to show the effectiveness of the two components. Code is at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "knapsack pruning with inner distillation"):
  with members:
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "contrastive representation distillation"
    - "importance estimation for neural network pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "densely connected search space for more flexible neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "vargnet variable group convolutional neural network for efficient embedded computing"
    - "exploring randomly wired neural networks for image recognition"
    - "customizable architecture search for semantic segmentation"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "evaluating the search phase of neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "one shot neural architecture search via self evaluated template network"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "densely connected search space for more flexible neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "teacher supervises students how to learn from partially labeled images for facial landmark detection"):
  with members:
    - "few example object detection with model communication"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "quantized densely connected u nets for efficient landmark localization"
    - "autoloss learning discrete schedules for alternate optimization"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"
    - "learning to reweight examples for robust deep learning"

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)