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: "do normalization layers in a deep convnet really need to be distinct"
  Abstract: Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towards understanding this phenomenon. We allow each convolutional layer to be stacked before a switchable normalization (SN) that learns to choose a normalizer from a pool of normalization methods. Through systematic experiments in ImageNet, COCO, Cityscapes, and ADE20K, we answer three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.

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"
    - "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-2 (induced by source paper "switchable whitening for deep representation learning"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "learning to adapt structured output space for semantic segmentation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "two at once enhancing learning and generalization capacities via ibn net"

Connected through hyperedge HE-3 (induced by source paper "differentiable learning to normalize via switchable normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "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-4 (induced by source paper "towards understanding regularization in batch normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "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)