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 skinny neural networks are not universal approximators"
  Abstract: In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options. These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures. In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate. This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.

Connected through hyperedge HE-1 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-2 (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"
    - "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"
    - "towards a mathematical understanding of the difficulty in learning with feedforward neural networks"
    - "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-3 (induced by source paper "universal approximation with deep narrow networks"):
  with members:
    - "on decision regions of narrow deep neural networks"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "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)