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: "streaming normalization towards simpler and more biologically plausible normalizations for online and recurrent learning"
  Abstract: This work was supported by the Center for Brains, Minds and Machines#R##N#(CBMM), funded by NSF STC award CCF - 1231216.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-4 (induced by source paper "switchable normalization for learning to normalize deep representation"):
  with members:
    - "switchable whitening for deep representation learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "iterative normalization beyond standardization towards efficient whitening"):
  with members:
    - "differentiable learning to normalize via switchable normalization"
    - "normalization of neural networks using analytic variance propagation"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-6 (induced by source paper "online normalization for training neural networks"):
  with members:
    - "mode normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "l2 regularization versus batch and weight normalization"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "instance normalization the missing ingredient for fast stylization"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "decorrelated batch normalization"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "riemannian approach to batch normalization"
    - "projection based weight normalization for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "relative natural gradient for learning large complex models"

Connected through hyperedge HE-8 (induced by source paper "normalization of neural networks using analytic variance propagation"):
  with members:
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "instance normalization the missing ingredient for fast stylization"
    - "improved techniques for training gans"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "normalizing the normalizers comparing and extending network normalization schemes"
    - "all you need is a good init"

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)