human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "diffusion improves graph learning"):
  Abstract: Graph convolution is the core of most Graph Neural Networks (GNNs) and usually approximated by message passing between direct (one-hop) neighbors. In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC). GDC leverages generalized graph diffusion, examples of which are the heat kernel and personalized PageRank. It alleviates the problem of noisy and often arbitrarily defined edges in real graphs. We show that GDC is closely related to spectral-based models and thus combines the strengths of both spatial (message passing) and spectral methods. We demonstrate that replacing message passing with graph diffusion convolution consistently leads to significant performance improvements across a wide range of models on both supervised and unsupervised tasks and a variety of datasets. Furthermore, GDC is not limited to GNNs but can trivially be combined with any graph-based model or algorithm (e.g. spectral clustering) without requiring any changes to the latter or affecting its computational complexity. Our implementation is available online.

Includes members:
  Member "pan path integral based convolution for deep graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural message passing on high order paths")
  Member "graph neural networks with convolutional arma filters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "simplifying graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "fast graph representation learning with pytorch geometric":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "optimizing generalized pagerank methods for seed expansion community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs")
  Member "node embedding with adaptive similarities for scalable learning over graphs":
    also connected through other hyperedges:
      (none)
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the expressive power of graph neural networks")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)