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: "ssn learning sparse switchable normalization via sparsestmax"
  Abstract: Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, leading to redundant computations compared to a single normalizer. #R##N#This work addresses this issue by presenting Sparse Switchable Normalization (SSN) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, Cityscapes, ADE20K, and Kinetics.

Connected through hyperedge HE-1 (induced by source paper "exemplar normalization for learning deep representation"):
  with members:
    - "channel equilibrium networks for learning deep representation"
    - "switchable whitening for deep representation learning"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "attentive normalization"
    - "positional normalization"
    - "global second order pooling convolutional networks"
    - "batch kalman normalization towards training deep neural networks with micro batches"

Connected through hyperedge HE-2 (induced by source paper "towards stabilizing batch statistics in backward propagation of batch normalization"):
  with members:
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "rethinking imagenet pre training"
    - "group normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "focal loss for dense object detection"
    - "megdet a large mini batch object detector"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "positional normalization"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-4 (induced by source paper "attentive normalization"):
  with members:
    - "instance level meta normalization"
    - "image synthesis from reconfigurable layout and style"
    - "searching for mobilenetv3"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fixup initialization residual learning without normalization"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "bag of tricks for image classification with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "weight standardization"):
  with members:
    - "a mean field theory of batch normalization"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "towards understanding regularization in batch normalization"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-6 (induced by source paper "differentiable learning to group channels via groupable convolutional neural networks"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-7 (induced by source paper "rethinking normalization and elimination singularity in neural networks"):
  with members:
    - "a mean field theory of batch normalization"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-8 (induced by source paper "adaptively sparse transformers"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "bert rediscovers the classical nlp pipeline"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "attention is not explanation"
    - "generating long sequences with sparse transformers"
    - "selective attention for context aware neural machine translation"
    - "adaptive attention span in transformers"

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)