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: "on the universality of invariant networks"
  Abstract: Constraining linear layers in neural networks to respect symmetry transformations from a group $G$ is a common design principle for invariant networks that has found many applications in machine learning. #R##N#In this paper, we consider a fundamental question that has received little attention to date: Can these networks approximate any (continuous) invariant function? #R##N#We tackle the rather general case where $G\leq S_n$ (an arbitrary subgroup of the symmetric group) that acts on $\mathbb{R}^n$ by permuting coordinates. This setting includes several recent popular invariant networks. We present two main results: First, $G$-invariant networks are universal if high-order tensors are allowed. Second, there are groups $G$ for which higher-order tensors are unavoidable for obtaining universality. #R##N#$G$-invariant networks consisting of only first-order tensors are of special interest due to their practical value. We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (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"
    - "a simple proof of the universality of invariant equivariant graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "can graph neural networks count substructures"):
  with members:
    - "composing molecules with multiple property constraints"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "g2sat learning to generate sat formulas"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "neural subgraph isomorphism counting"

Connected through hyperedge HE-4 (induced by source paper "generalization and representational limits of graph neural networks"):
  with members:
    - "directional message passing for molecular graphs"
    - "efficient probabilistic logic reasoning with graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "graph transformer networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "a survey on graph kernels"
    - "what graph neural networks cannot learn depth vs width"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "random features strengthen graph neural networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "kgat knowledge graph attention network for recommendation"
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "what graph neural networks cannot learn depth vs width"
    - "provably powerful graph networks"

Connected through hyperedge HE-7 (induced by source paper "universal equivariant multilayer perceptrons"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on universal equivariant set networks"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-8 (induced by source paper "solving np hard problems on graphs with extended alphago zero"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "provably powerful graph networks"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "semi supervised classification with graph convolutional networks"

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)