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 invariant maps by neural networks"
  Abstract: We describe generalizations of the universal approximation theorem for neural networks to maps invariant or equivariant with respect to linear representations of groups. Our goal is to establish network-like computational models that are both invariant/equivariant and provably complete in the sense of their ability to approximate any continuous invariant/equivariant map. Our contribution is three-fold. First, in the general case of compact groups we propose a construction of a complete invariant/equivariant network using an intermediate polynomial layer. We invoke classical theorems of Hilbert and Weyl to justify and simplify this construction; in particular, we describe an explicit complete ansatz for approximation of permutation-invariant maps. Second, we consider groups of translations and prove several versions of the universal approximation theorem for convolutional networks in the limit of continuous signals on euclidean spaces. Finally, we consider 2D signal transformations equivariant with respect to the group SE(2) of rigid euclidean motions. In this case we introduce the "charge--conserving convnet" -- a convnet-like computational model based on the decomposition of the feature space into isotypic representations of SO(2). We prove this model to be a universal approximator for continuous SE(2)--equivariant signal transformations.

Connected through hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "on the limitations of representing functions on sets"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"

Connected through hyperedge HE-2 (induced by source paper "set2graph learning graphs from sets"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "meta learning to cluster"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a computationally efficient neural network invariant to the action of symmetry subgroups"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "geometric deep learning going beyond euclidean data"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "graph neural networks exponentially lose expressive power for node classification"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "on the universality of invariant networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "simplifying graph convolutional networks"
    - "stability and generalization of graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-5 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "regularized evolution for image classifier architecture search"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "universality of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "pine universal deep embedding for graph nodes via partial permutation invariant set functions"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "deep sets"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "struc2vec learning node representations from structural identity"

Connected through hyperedge HE-7 (induced by source paper "on the equivalence between graph isomorphism testing and function approximation with gnns"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "relational pooling for graph representations"
    - "how powerful are graph neural networks"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-8 (induced by source paper "universal invariant and equivariant graph neural networks"):
  with members:
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "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"
    - "invariant and equivariant graph networks"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"

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.NE(Neural and Evolutionary Computing)