human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "universal approximation with deep narrow networks"):
  Abstract: The classical Universal Approximation Theorem certifies that the universal approximation property holds for the class of neural networks of arbitrary width. Here we consider the natural `dual' theorem for width-bounded networks of arbitrary depth. Precisely, let $n$ be the number of inputs neurons, $m$ be the number of output neurons, and let $\rho$ be any nonaffine continuous function, with a continuous nonzero derivative at some point. Then we show that the class of neural networks of arbitrary depth, width $n + m + 2$, and activation function $\rho$, exhibits the universal approximation property with respect to the uniform norm on compact subsets of $\mathbb{R}^n$. This covers every activation function possible to use in practice; in particular this includes polynomial activation functions, making this genuinely different to the classical case. We go on to establish some natural extensions of this result. Firstly, we show an analogous result for a certain class of nowhere differentiable activation functions. Secondly, we establish an analogous result for noncompact domains, by showing that deep narrow networks with the ReLU activation function exhibit the universal approximation property with respect to the $p$-norm on $\mathbb{R}^n$. Finally, we show that width of only $n + m + 1$ suffices for `most' activation functions (whilst it is known that width of $n + m - 1$ does not suffice in general).

Includes members:
  Member "deep skinny neural networks are not universal approximators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study")
  Member "on decision regions of narrow deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geometry and topology of deep neural networks decision boundaries")
  Member "neural networks should be wide enough to learn disconnected decision regions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geometry and topology of deep neural networks decision boundaries")
  Member "resnet with one neuron hidden layers is a universal approximator":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "the expressive power of neural networks a view from the width":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")

Question: Please tell me which class the source paper 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)