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: "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.

Connected through hyperedge HE-1 (induced by source paper "set2graph learning graphs from sets"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "meta learning to cluster"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on universal equivariant set networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "clustering with deep learning taxonomy and new methods"
    - "attention based deep multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "universal equivariant multilayer perceptrons"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "on universal equivariant set networks"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-3 (induced by source paper "are transformers universal approximators of sequence to sequence functions"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "pay less attention with lightweight and dynamic convolutions"
    - "analyzing the structure of attention in a transformer language model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "on the turing completeness of modern neural network architectures"
    - "roberta a robustly optimized bert pretraining approach"
    - "resnet with one neuron hidden layers is a universal approximator"

Connected through hyperedge HE-4 (induced by source paper "set aggregation network as a trainable pooling layer"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "deep sets"
    - "how powerful are graph neural networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "attention is all you need"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "universal invariant and equivariant graph neural networks"):
  with members:
    - "on the universality of invariant networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph networks as learnable physics engines for inference and control"
    - "universal approximations of invariant maps by neural networks"
    - "invariant and equivariant graph networks"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"

Connected through hyperedge HE-6 (induced by source paper "a simple proof of the universality of invariant equivariant graph neural networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "making convolutional networks shift invariant again"
    - "on the universality of invariant networks"
    - "universal approximations of invariant maps by neural networks"
    - "deep sets"
    - "invariant and equivariant graph networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "on universal equivariant set networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "provably powerful graph networks"
    - "deepgcns can gcns go as deep as cnns"
    - "universal approximations of invariant maps by neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "neural network with unbounded activation functions is universal approximator"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "learning set equivariant functions with swarm mappings"):
  with members:
    - "generating long sequences with sparse transformers"
    - "universal approximations of invariant maps by neural networks"
    - "attention is all you need"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning multiagent communication with backpropagation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)