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: "towards a mathematical understanding of the difficulty in learning with feedforward neural networks"
  Abstract: Training deep neural networks for solving machine learning problems is one great challenge in the field, mainly due to its associated optimisation problem being highly non-convex. Recent developments have suggested that many training algorithms do not suffer from undesired local minima under certain scenario, and consequently led to great efforts in pursuing mathematical explanations for such observations. This work provides an alternative mathematical understanding of the challenge from a smooth optimisation perspective. By assuming exact learning of finite samples, sufficient conditions are identified via a critical point analysis to ensure any local minimum to be globally minimal as well. Furthermore, a state of the art algorithm, known as the Generalised Gauss-Newton (GGN) algorithm, is rigorously revisited as an approximate Newton's algorithm, which shares the property of being locally quadratically convergent to a global minimum under the condition of exact learning.

Connected through hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "motion attentive transition for zero shot video object segmentation"
    - "self supervised learning for video correspondence flow"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "multi source weak supervision for saliency detection"
    - "learning correspondence from the cycle consistency of time"

Connected through hyperedge HE-2 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "learning strict identity mappings in deep residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "a geometric view of optimal transportation and generative model"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-3 (induced by source paper "the expressivity and training of deep neural networks toward the edge of chaos"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "a mean field theory of batch normalization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "how powerful are graph neural networks"
    - "deriving neural architectures from sequence and graph kernels"
    - "mean field residual networks on the edge of chaos"
    - "representation learning on graphs methods and applications"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "loss surface modality of feed forward neural network architectures"):
  with members:
    - "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"
    - "deep skinny neural networks are not universal approximators"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "depth creates no bad local minima"
    - "the loss surface of deep and wide neural networks"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"):
  with members:
    - "sharp minima can generalize for deep nets"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep learning in neural networks"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

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)