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: "subgraph matching kernels for attributed graphs"
  Abstract: We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by a flexible scoring scheme comparing vertex and edge attributes by kernels. We show that subgraph matching kernels generalize several known kernels. To compute the kernel we propose a graph-theoretical algorithm inspired by a classical relation between common subgraphs of two graphs and cliques in their product graph observed by Levi (1973). Encouraging experimental results on a classification task of real-world graphs are presented.

Connected through hyperedge HE-1 (induced by source paper "understanding the power of persistence pairing via permutation test"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "on the metric distortion of embedding persistence diagrams into separable hilbert spaces"
    - "a simple yet effective baseline for non attributed graph classification"
    - "deep sets"
    - "on the metric distortion of embedding persistence diagrams into reproducing kernel hilbert spaces"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"
    - "ricci curvature of the internet topology"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical transitive aligned graph kernel for un attributed graphs"):
  with members:
    - "a quantum inspired similarity measure for the analysis of complete weighted graphs"
    - "learning convolutional neural networks for graphs"
    - "diffusion convolutional neural networks"
    - "learning by transduction"
    - "graph kernels between point clouds"

Connected through hyperedge HE-3 (induced by source paper "haar graph pooling"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "fast haar transforms for graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "graph convolutional networks with eigenpooling"
    - "self attention graph pooling"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "understanding isomorphism bias in graph data sets"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "do we train on test data purging cifar of near duplicates"
    - "a survey on graph kernels"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "do imagenet classifiers generalize to imagenet"
    - "fast graph representation learning with pytorch geometric"
    - "provably powerful graph networks"
    - "stability and generalization of graph convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "a unifying view of explicit and implicit feature maps of graph kernels"):
  with members:
    - "a survey on graph kernels"
    - "graph convolutional neural networks for web scale recommender systems"
    - "tree based kernel for graphs with continuous attributes"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"

Connected through hyperedge HE-6 (induced by source paper "haarpooling graph pooling with compressive haar basis"):
  with members:
    - "fast haar transforms for graph neural networks"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "graph convolutional networks with eigenpooling"
    - "self attention graph pooling"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-7 (induced by source paper "a survey on graph kernels"):
  with members:
    - "a unifying view of explicit and implicit feature maps of graph kernels"
    - "deep weisfeiler lehman assignment kernels via multiple kernel learning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "faster kernels for graphs with continuous attributes via hashing"

Connected through hyperedge HE-8 (induced by source paper "solving graph compression via optimal transport"):
  with members:
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "learning small predictors"
    - "how powerful are graph neural networks"
    - "spectrally approximating large graphs with smaller graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "continuous flow graph transportation distances"
    - "learning with a wasserstein loss"

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)