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: "spectral projector based graph fourier transforms"
  Abstract: This paper considers the definition of the graph Fourier transform (GFT) and of the spectral decomposition of graph signals. Current literature does not address the lack of unicity of the GFT. The GFT is the mapping from the signal set into its representation by a direct sum of irreducible shift invariant subspaces: 1) this   decomposition  may not be unique; and 2) there is freedom in the  choice of basis  for each component subspace. These issues are particularly relevant when the graph shift has repeated eigenvalues as is the case in many real-world applications; by ignoring them, there is no way of knowing if different researchers are using the same definition of the GFT and whether their results are comparable or not. The paper presents how to resolve the above degrees of freedom. We develop a  quasi -coordinate free definition of the GFT and graph spectral decomposition of graph signals that we implement through oblique spectral projectors. We present properties of the GFT and of the spectral projectors and discuss a generalized Parseval's inequality. An illustrative example for a large real-world urban traffic dataset is provided.

Connected through hyperedge HE-1 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "blind identification of graph filters"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "subgraph based filterbanks for graph signals"

Connected through hyperedge HE-2 (induced by source paper "topology adaptive graph convolutional networks"):
  with members:
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "distributed clock skew and offset estimation in wireless sensor networks asynchronous algorithm and convergence analysis"
    - "discriminative embeddings of latent variable models for structured data"
    - "revisiting semi supervised learning with graph embeddings"
    - "diffusion convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "identifying influential links for event propagation on twitter a network of networks approach"):
  with members:
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "diffusion and superposition distances for signals supported on networks"
    - "information diffusion and external influence in 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.SI(Social and Information Networks)