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: "optimization on submanifolds of convolution kernels in cnns"
  Abstract: Kernel normalization methods have been employed to improve robustness of optimization methods to reparametrization of convolution kernels, covariate shift, and to accelerate training of Convolutional Neural Networks (CNNs). However, our understanding of theoretical properties of these methods has lagged behind their success in applications. We develop a geometric framework to elucidate underlying mechanisms of a diverse range of kernel normalization methods. Our framework enables us to expound and identify geometry of space of normalized kernels. We analyze and delineate how state-of-the-art kernel normalization methods affect the geometry of search spaces of the stochastic gradient descent (SGD) algorithms in CNNs. Following our theoretical results, we propose a SGD algorithm with assurance of almost sure convergence of the methods to a solution at single minimum of classification loss of CNNs. Experimental results show that the proposed method achieves state-of-the-art performance for major image classification benchmarks with CNNs.

Connected through hyperedge HE-1 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "feature mask network for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"

Connected through hyperedge HE-2 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"

Connected through hyperedge HE-3 (induced by source paper "orthogonal deep neural networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the singular values of convolutional layers"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "on the effect of pooling on the geometry of representations"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "fine grained optimization of deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "exploring generalization in deep learning"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "orthogonal wasserstein gans"):
  with members:
    - "on gans and gmms"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "riemannian adaptive optimization methods"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "generalized backpropagation etude de cas orthogonality"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "recurrent orthogonal networks and long memory tasks"

Connected through hyperedge HE-7 (induced by source paper "can we gain more from orthogonality regularizations in training deep cnns"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "spectral normalization for generative adversarial networks"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "generalized backpropagation etude de cas orthogonality"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"):
  with members:
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning to refine object segments"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

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.CV(Computer Vision and Pattern Recognition)