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 "multi centrality graph spectral decompositions and their application to cyber intrusion detection"):
  Abstract: Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the Fiedler vector of centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles of graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.

Includes members:
  Member "a spectral framework for anomalous subgraph detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fraud detection in networks state of the art")
  Member "robust principal component analysis on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wasserstein based graph alignment")
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "signal recovery on graphs variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering")
  Member "learning parametric dictionaries for signals on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "phase transitions in spectral community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)