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: "weight normalization a simple reparameterization to accelerate training of deep neural networks"
  Abstract: We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "towards federated learning at scale system design"
    - "when does label smoothing help"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "adapted center and scale prediction more stable and more accurate"):
  with members:
    - "psc net learning part spatial co occurence for occluded pedestrian detection"
    - "semantic head enhanced pedestrian detection in a crowd"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "foveabox beyond anchor based object detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"

Connected through hyperedge HE-3 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-6 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-7 (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-8 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "a mean field theory of batch normalization"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"

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)