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: "understanding symmetries in deep networks"
  Abstract: Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that a commonly used deep network, which uses convolution, batch normalization, reLU, max-pooling, and sub-sampling pipeline, possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights. We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold. Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows that the proposed updates improve the test performance beyond what is achieved with batch normalization and without sacrificing the computational efficiency of the weight updates.

Connected through hyperedge HE-1 (induced by source paper "leader stochastic gradient descent for distributed training of deep learning models"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "understanding the loss surface of neural networks for binary classification"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "integrated model batch and domain parallelism in training neural networks"
    - "parle parallelizing stochastic gradient descent"
    - "scaling sgd batch size to 32k for imagenet training"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"

Connected through hyperedge HE-2 (induced by source paper "sharp minima can generalize for deep nets"):
  with members:
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "wav2letter an end to end convnet based speech recognition system"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "understanding deep learning requires rethinking generalization"
    - "density estimation using real nvp"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "improved techniques for training gans"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "riemannian approach to batch normalization"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "deep networks with stochastic depth"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "path sgd path normalized optimization in deep neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "density estimation using real nvp"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "improving variational inference with inverse autoregressive flow"
    - "learning functions across many orders of magnitudes"
    - "identity mappings in deep residual networks"
    - "generating images with recurrent adversarial networks"
    - "continuous deep q learning with model based acceleration"
    - "colorful image colorization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "segnet a deep convolutional encoder decoder architecture for image segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-6 (induced by source paper "symmetry invariant optimization in deep networks"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "path sgd path normalized optimization in deep neural networks"
    - "riemannian metrics for neural networks i feedforward networks"
    - "riemannian metrics for neural networks ii recurrent networks and learning symbolic data sequences"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "revisiting natural gradient for deep networks"
    - "do deep nets really need to be deep"

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)