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: "switchable whitening for deep representation learning"
  Abstract: Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixels. Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods. SW learns to switch among these operations in an end-to-end manner. It has several advantages. First, SW adaptively selects appropriate whitening or standardization statistics for different tasks (see Fig.1), making it well suited for a wide range of tasks without manual design. Second, by integrating benefits of different normalizers, SW shows consistent improvements over its counterparts in various challenging benchmarks. Third, SW serves as a useful tool for understanding the characteristics of whitening and standardization techniques. We show that SW outperforms other alternatives on image classification (CIFAR-10/100, ImageNet), semantic segmentation (ADE20K, Cityscapes), domain adaptation (GTA5, Cityscapes), and image style transfer (COCO). For example, without bells and whistles, we achieve state-of-the-art performance with 45.33% mIoU on the ADE20K dataset. Code is available at this https URL.

Connected through hyperedge HE-1 (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"
    - "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 "channel equilibrium networks for learning deep representation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "side aware boundary localization for more precise object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "nas fcos fast neural architecture search for object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"

Connected through hyperedge HE-4 (induced by source paper "switchable normalization for learning to normalize deep representation"):
  with members:
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "arcface additive angular margin loss for deep face recognition"
    - "spectral normalization for generative adversarial networks"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "efficient neural architecture search via parameter sharing"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-5 (induced by source paper "differentiable learning to normalize via switchable normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "decorrelated batch normalization"
    - "efficient neural architecture search via parameter sharing"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "towards understanding regularization in batch normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "differentiable learning to normalize via switchable normalization"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "electron proton dynamics in deep learning"
    - "l2 regularization versus batch and weight normalization"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"

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)