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: "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.

Connected through hyperedge HE-1 (induced by source paper "relative hausdorff distance for network analysis"):
  with members:
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "computing the gromov hausdorff distance for metric trees"
    - "graph based anomaly detection and description a survey"
    - "quantification and comparison of degree distributions in complex networks"
    - "information spreading in dynamic graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "name disambiguation in anonymized graphs using network embedding"):
  with members:
    - "bayesian non exhaustive classification a case study online name disambiguation using temporal record streams"
    - "nous construction and querying of dynamic knowledge graphs"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "feature selection for classification under anonymity constraint"
    - "incremental method for spectral clustering of increasing orders"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-3 (induced by source paper "when labels fall short property graph simulation via blending of network structure and vertex attributes"):
  with members:
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-4 (induced by source paper "multilayer spectral graph clustering via convex layer aggregation"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "information extraction from larger multi layer social networks"
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "universal phase transition in community detectability under a stochastic block model"
    - "deep community detection"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social 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)