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 "attentive normalization"):
  Abstract: In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous with significant performance improvement shown in a vast amount of tasks. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between, and thus harness the best of, the two schema. We present Attentive Normalization (AN) which generalizes the common affine transformation component in the vanilla feature normalization. Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way. The weights are learned by leveraging feature attention. AN introduces negligible extra parameters and computational cost (i.e., light-weight). AN can be used as a drop-in replacement for any feature normalization technique which includes the affine transformation component. In experiments, we test the proposed AN using three representative neural architectures (ResNets, MobileNets-v2 and AOGNets) in the ImageNet-1000 classification benchmark and the MS-COCO 2107 object detection and instance segmentation benchmark. AN obtains consistent performance improvement for different neural architectures in both benchmarks with absolute increase of top-1 accuracy in ImageNet-1000 between 0.5% and 2.0%, and absolute increase up to 1.8% and 2.2% for bounding box and mask AP in MS-COCO respectively. The source codes are publicly available(Classification in ImageNet: \url{https://github.com/iVMCL/AOGNets-v2} and Detection in MS-COCO: \url{https://github.com/iVMCL/AttentiveNorm\_Detection}}).

Includes members:
  Member "ssn learning sparse switchable normalization via sparsestmax":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exemplar normalization for learning deep representation")
  Member "instance level meta normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "instance enhancement batch normalization an adaptive regulator of batch noise")
  Member "image synthesis from reconfigurable layout and style":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object centric image generation from layouts")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "fixup initialization residual learning without normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object centric image generation from layouts")

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)