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: "recurrent batch normalization"
  Abstract: We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization.

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 "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "policy optimization as wasserstein gradient flows"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-3 (induced by source paper "extended batch normalization"):
  with members:
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-5 (induced by source paper "transformer xl attentive language models beyond a fixed length context"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-6 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "multiplicative lstm for sequence modelling"

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)