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: "neural networks should be wide enough to learn disconnected decision regions"
  Abstract: In the recent literature the important role of depth in deep learning has been emphasized. In this paper we argue that sufficient width of a feedforward network is equally important by answering the simple question under which conditions the decision regions of a neural network are connected. It turns out that for a class of activation functions including leaky ReLU, neural networks having a pyramidal structure, that is no layer has more hidden units than the input dimension, produce necessarily connected decision regions. This implies that a sufficiently wide layer is necessary to produce disconnected decision regions. We discuss the implications of this result for the construction of neural networks, in particular the relation to the problem of adversarial manipulation of classifiers.

Connected through hyperedge HE-1 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-2 (induced by source paper "deepsquare boosting the learning power of deep convolutional neural networks with elementwise square operators"):
  with members:
    - "global second order pooling convolutional networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "mnasnet platform aware neural architecture search for mobile"

Connected through hyperedge HE-3 (induced by source paper "robust deep neural networks inspired by fuzzy logic"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "understanding adversarial robustness the trade off between minimum and average margin"
    - "adversarial training versus weight decay"
    - "sparse dnns with improved adversarial robustness"
    - "adversarially robust generalization requires more data"
    - "provable robustness of relu networks via maximization of linear regions"

Connected through hyperedge HE-4 (induced by source paper "two hidden layer feedforward neural networks are universal approximators a constructive approach"):
  with members:
    - "the expressive power of neural networks a view from the width"
    - "benefits of depth in neural networks"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "on the depth of deep neural networks a theoretical view"

Connected through hyperedge HE-5 (induced by source paper "universal approximation with deep narrow networks"):
  with members:
    - "deep skinny neural networks are not universal approximators"
    - "on decision regions of narrow deep neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-6 (induced by source paper "empirical bounds on linear regions of deep rectifier networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "on decision regions of narrow deep neural networks"
    - "tropical geometry of deep neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "provable robustness of relu networks via maximization of linear regions"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-7 (induced by source paper "deep skinny neural networks are not universal approximators"):
  with members:
    - "expressive power of recurrent neural networks"
    - "the loss surface of deep and wide neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-8 (induced by source paper "a differential topological view of challenges in learning with feedforward neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "i revnet deep invertible networks"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "exploring generalization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "the loss surface of deep and wide neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "the expressive power of neural networks a view from the width"

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)