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: "normalizing the normalizers comparing and extending network normalization schemes"
  Abstract: Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural networks. On the other hand, layer normalization normalizes the activations across all activities within a layer. This was shown to work well in the recurrent setting. In this paper we propose a unified view of normalization techniques, as forms of divisive normalization, which includes layer and batch normalization as special cases. Our second contribution is the finding that a small modification to these normalization schemes, in conjunction with a sparse regularizer on the activations, leads to significant benefits over standard normalization techniques. We demonstrate the effectiveness of our unified divisive normalization framework in the context of convolutional neural nets and recurrent neural networks, showing improvements over baselines in image classification, language modeling as well as super-resolution.

Connected through hyperedge HE-1 (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-2 (induced by source paper "weight standardization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-3 (induced by source paper "why not to use zero imputation correcting sparsity bias in training neural networks"):
  with members:
    - "misgan learning from incomplete data with generative adversarial networks"
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "dropblock a regularization method for convolutional networks"
    - "deep sets"
    - "self normalizing neural networks"
    - "mida multiple imputation using denoising autoencoders"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "deepsignals predicting intent of drivers through visual signals"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "mask r cnn"
    - "accurate single stage detector using recurrent rolling convolution"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "deep watershed transform for instance segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (induced by source paper "inherent weight normalization in stochastic neural networks"):
  with members:
    - "self normalizing neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "emnist an extension of mnist to handwritten letters"
    - "learning to learn by gradient descent by gradient descent"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "stochastic synapses enable efficient brain inspired learning machines"

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)