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 approximations of permutation invariant equivariant functions by deep neural networks"):
  Abstract: In this paper,we develop a theory of the relationship between permutation ($S_n$-) invariant/equivariant functions and deep neural networks. As a result, we prove an permutation invariant/equivariant version of the universal approximation theorem, i.e $S_n$-invariant/equivariant deep neural networks. The equivariant models are consist of stacking standard single-layer neural networks $Z_i:X \to Y$ for which every $Z_i$ is $S_n$-equivariant with respect to the actions of $S_n$ . The invariant models are consist of stacking equivariant models and standard single-layer neural networks $Z_i:X \to Y$ for which every $Z_i$ is $S_n$-invariant with respect to the actions of $S_n$ . These are universal approximators to $S_n$-invariant/equivariant functions. The above notation is mathematically natural generalization of the models in \cite{deepsets}. We also calculate the number of free parameters appeared in these models. As a result, the number of free parameters appeared in these models is much smaller than the one of the usual models. Hence, we conclude that although the free parameters of the invariant/equivarint models are exponentially fewer than the one of the usual models, the invariant/equivariant models can approximate the invariant/equivariant functions to arbitrary accuracy. This gives us an understanding of why the invariant/equivariant models designed in [Zaheer et al. 2018] work well.

Includes members:
  Member "universal invariant and equivariant graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "on the universality of invariant networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "universal approximations of invariant maps by neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on learning sets of symmetric elements")
  Member "invariant and equivariant graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "covariant compositional networks for learning graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "neural network with unbounded activation functions is universal approximator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometric deep learning for computational mechanics part i anisotropic hyperelasticity")
  Member "the expressive power of neural networks a view from the width":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "group equivariant convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

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)