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: "wasserstein weisfeiler lehman graph kernels"
  Abstract: Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empirical success, most graph kernels use a naive aggregation of the final set of substructures, usually a sum or average, thereby potentially discarding valuable information about the distribution of individual components. Furthermore, only a limited instance of these approaches can be extended to continuously attributed graphs. We propose a novel method that relies on the Wasserstein distance between the node feature vector distributions of two graphs, which allows to find subtler differences in data sets by considering graphs as high-dimensional objects, rather than simple means. We further propose a Weisfeiler-Lehman inspired embedding scheme for graphs with continuous node attributes and weighted edges, enhance it with the computed Wasserstein distance, and thus improve the state-of-the-art prediction performance on several graph classification tasks.

Connected through hyperedge HE-1 (induced by source paper "graph neural distance metric learning with graph bert"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "graph bert only attention is needed for learning graph representations"
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "structure fusion based on graph convolutional networks for semi supervised classification"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-2 (induced by source paper "neural subgraph isomorphism counting"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "how powerful are graph neural networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "persistent homology as stopping criterion for natural neighbor interpolation"):
  with members:
    - "sliced wasserstein kernel for persistence diagrams"
    - "delaunay triangulation of manifolds"
    - "the geometric stability of voronoi diagrams with respect to small changes of the sites"
    - "computing multidimensional persistence"

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)