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: "provably powerful graph networks"
  Abstract: Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many simple instances of graphs are indistinguishable by the 1-WL test. #R##N#In search for more expressive graph learning models we build upon the recent k-order invariant and equivariant graph neural networks (Maron et al. 2019a,b) and present two results: #R##N#First, we show that such k-order networks can distinguish between non-isomorphic graphs as good as the k-WL tests, which are provably stronger than the 1-WL test for k>2. This makes these models strictly stronger than message passing models. Unfortunately, the higher expressiveness of these models comes with a computational cost of processing high order tensors. #R##N#Second, setting our goal at building a provably stronger, simple and scalable model we show that a reduced 2-order network containing just scaled identity operator, augmented with a single quadratic operation (matrix multiplication) has a provable 3-WL expressive power. Differently put, we suggest a simple model that interleaves applications of standard Multilayer-Perceptron (MLP) applied to the feature dimension and matrix multiplication. We validate this model by presenting state of the art results on popular graph classification and regression tasks. To the best of our knowledge, this is the first practical invariant/equivariant model with guaranteed 3-WL expressiveness, strictly stronger than message passing models.

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"
    - "on the universality of invariant 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"
    - "on the universality of invariant networks"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"

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"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "a survey on graph kernels"

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"
    - "on the universality of invariant 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"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "what graph neural networks cannot learn depth vs width"

Connected through hyperedge HE-7 (induced by source paper "directional message passing for molecular graphs"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the convergence of adam and beyond"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-8 (induced by source paper "efficient probabilistic logic reasoning with graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "probabilistic logic neural networks for reasoning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "one shot relational learning for knowledge graphs"
    - "knowledge base completion baselines strike back"
    - "gated graph sequence neural 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)