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: "deep neural networks with multi branch architectures are less non convex"
  Abstract: Several recently proposed architectures of neural networks such as ResNeXt, Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple branches and have demonstrated improved performance in many applications. We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap. The duality gap measures the degree of intrinsic non-convexity of an optimization problem: smaller gap in relative value implies lower degree of intrinsic non-convexity. The challenge is to quantitatively measure the duality gap of highly non-convex problems such as deep neural networks. In this work, we provide strong guarantees of this quantity for two classes of network architectures. For the neural networks with arbitrary activation functions, multi-branch architecture and a variant of hinge loss, we show that the duality gap of both population and empirical risks shrinks to zero as the number of branches increases. This result sheds light on better understanding the power of over-parametrization where increasing the network width tends to make the loss surface less non-convex. For the neural networks with linear activation function and $\ell_2$ loss, we show that the duality gap of empirical risk is zero. Our two results work for arbitrary depths and adversarial data, while the analytical techniques might be of independent interest to non-convex optimization more broadly. Experiments on both synthetic and real-world datasets validate our results.

Connected through hyperedge HE-1 (induced by source paper "theoretically principled trade off between robustness and accuracy"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "adversarial vision challenge"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-2 (induced by source paper "on connected sublevel sets in deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "elimination of all bad local minima in deep learning"):
  with members:
    - "effect of depth and width on local minima in deep learning"
    - "deep learning for classical japanese literature"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "are resnets provably better than linear predictors"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-4 (induced by source paper "generalization error in deep learning"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "exploring generalization in deep learning"
    - "mathematics of deep learning"

Connected through hyperedge HE-5 (induced by source paper "on the loss landscape of a class of deep neural networks with no bad local valleys"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"
    - "exploring generalization in deep learning"
    - "recovery guarantees for one hidden layer neural networks"
    - "failures of gradient based deep learning"
    - "depth creates no bad local minima"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-6 (induced by source paper "stackelberg gan towards provable minimax equilibrium via multi generator architectures"):
  with members:
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "matrix completion and related problems via strong duality"
    - "magan margin adaptation for generative adversarial networks"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"

Connected through hyperedge HE-7 (induced by source paper "mckernel approximate kernel expansions in log linear time through randomization"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "deep semi random features for nonlinear function approximation"
    - "self normalizing neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "large margin object tracking with circulant feature maps"
    - "theory ii landscape of the empirical risk in deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "learning scalable deep kernels with recurrent structure"

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)