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: "evalnorm estimating batch normalization statistics for evaluation"
  Abstract: Batch normalization (BN) has been very effective for deep learning and is widely used. However, when training with small minibatches, models using BN exhibit a significant degradation in performance. In this paper we study this peculiar behavior of BN to gain a better understanding of the problem, and identify a potential cause based on a statistical insight. We propose `EvalNorm' to address the issue by estimating corrected normalization statistics to use for BN during evaluation. EvalNorm supports online estimation of the corrected statistics while the model is being trained, and it does not affect the training scheme of the model. As a result, an added advantage of EvalNorm is that it can be used with existing pre-trained models allowing them to benefit from our method. EvalNorm yields large gains for models trained with smaller batches. Our experiments show that EvalNorm performs 6.18% (absolute) better than vanilla BN for a batchsize of 2 on ImageNet validation set and from 1.5 to 7.0 points (absolute) gain on the COCO object detection benchmark across a variety of setups.

Connected through hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers"):
  with members:
    - "towards stabilizing batch statistics in backward propagation of batch normalization"
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a tensorized transformer for language modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "root mean square layer normalization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding and improving layer normalization"

Connected through hyperedge HE-2 (induced by source paper "extended batch normalization"):
  with members:
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "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-3 (induced by source paper "towards stabilizing batch statistics in backward propagation of batch normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "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-4 (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"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "decorrelated batch normalization"

Connected through hyperedge HE-5 (induced by source paper "filter response normalization layer eliminating batch dependence in the training of deep neural networks"):
  with members:
    - "rethinking imagenet pre training"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "focal loss for dense object detection"
    - "megdet a large mini batch object detector"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "instance normalization the missing ingredient for fast stylization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

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)