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: "faster kernels for graphs with continuous attributes via hashing"
  Abstract: While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous attributes into discrete labels using randomized hash functions. We illustrate hash graph kernels for the Weisfeiler-Lehman subtree kernel and for the shortest-path kernel. The resulting novel graph kernels are shown to be, both, able to handle graphs with continuous attributes and scalable to large graphs and data sets. This is supported by our theoretical analysis and demonstrated by an extensive experimental evaluation.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-4 (induced by source paper "wasserstein weisfeiler lehman graph kernels"):
  with members:
    - "gromov wasserstein learning for graph matching and node embedding"
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "semi supervised classification with graph convolutional networks"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-5 (induced by source paper "towards a practical k dimensional weisfeiler leman algorithm"):
  with members:
    - "a survey on graph kernels"
    - "provably powerful graph networks"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"
    - "graph isomorphism in quasipolynomial time"
    - "graphs identified by logics with counting"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "practical graph isomorphism ii"
    - "dimension reduction via colour refinement"

Connected through hyperedge HE-6 (induced by source paper "graph classification with 2d convolutional neural networks"):
  with members:
    - "attributed social network embedding"
    - "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"
    - "node2vec scalable feature learning for networks"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "generalizing the convolution operator to extend cnns to irregular domains"

Connected through hyperedge HE-7 (induced by source paper "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"):
  with members:
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
    - "spectral networks and locally connected networks on graphs"
    - "subgraph matching kernels for attributed graphs"
    - "random feature maps for dot product kernels"

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)