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: "differentiable learning to normalize via switchable normalization"
  Abstract: We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been made available in this https URL.

Connected through hyperedge HE-1 (induced by source paper "adapted center and scale prediction more stable and more accurate"):
  with members:
    - "psc net learning part spatial co occurence for occluded pedestrian detection"
    - "semantic head enhanced pedestrian detection in a crowd"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "foveabox beyond anchor based object detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"

Connected through hyperedge HE-2 (induced by source paper "exemplar normalization for learning deep representation"):
  with members:
    - "channel equilibrium networks for learning deep representation"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "channel equilibrium networks for learning deep representation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "switchable whitening for deep representation learning"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "extended batch normalization"):
  with members:
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "towards understanding regularization in batch normalization"
    - "generalized batch normalization towards accelerating deep neural networks"
    - "self normalizing neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "positional normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "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"
    - "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-6 (induced by source paper "four things everyone should know to improve batch normalization"):
  with members:
    - "truth or backpropaganda an empirical investigation of deep learning theory"
    - "fixup initialization residual learning without normalization"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "decorrelated batch normalization"

Connected through hyperedge HE-7 (induced by source paper "attentive normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "weight standardization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch 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"

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)