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: "instance level meta normalization"
  Abstract: This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanism and has several good properties. It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization, Layer Normalization, or Group Normalization. ILM~Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used. The experimental results show that ILM~Norm well adapts to different network architectures and tasks, and it consistently improves the performance of the original models. The code is available at url{this https URL.

Connected through hyperedge HE-1 (induced by source paper "attentive normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "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-2 (induced by source paper "instance enhancement batch normalization an adaptive regulator of batch noise"):
  with members:
    - "a quantitative analysis of the effect of batch normalization on gradient descent"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "selective kernel networks"
    - "dianet dense and implicit attention network"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "towards understanding regularization in batch normalization"

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.LG(Machine Learning)