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: "a survey on graph kernels"
  Abstract: Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner's guide to kernel-based graph classification.

Connected through hyperedge HE-1 (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"
    - "what graph neural networks cannot learn depth vs width"

Connected through hyperedge HE-2 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures"):
  with members:
    - "self attention graph pooling"
    - "a closer look at few shot classification"
    - "a comprehensive survey on graph neural networks"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "universal function approximation on graphs using multivalued functions"):
  with members:
    - "graph filtration learning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "understanding deep neural networks with rectified linear units"
    - "graph isomorphism in quasipolynomial time"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "counting bounded tree depth homomorphisms"):
  with members:
    - "a simple proof of the universality of invariant equivariant graph neural networks"
    - "a finite model theoretic view on propositional proof complexity"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graph isomorphism in quasipolynomial time"
    - "limitations of algebraic approaches to graph isomorphism testing"
    - "graph isomorphism parameterized by elimination distance to bounded degree"
    - "pebble games and linear equations"

Connected through hyperedge HE-5 (induced by source paper "local wl invariance and hidden shades of regularity"):
  with members:
    - "identifiability of graphs with small color classes by the weisfeiler leman algorithm"
    - "on the weisfeiler leman dimension of fractional packing"
    - "on weisfeiler leman invariance subgraph counts and related graph properties"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "benchmark graphs for practical graph isomorphism"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-6 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "fast graph representation learning with pytorch geometric"
    - "relational pooling for graph representations"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "graph matching networks for learning the similarity of graph structured objects"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep sets"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "unsupervised universal self attention network for graph classification"):
  with members:
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "discriminative structural graph classification"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep learning on graphs a survey"
    - "relational inductive biases deep learning and graph 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)