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: "extracting spatial information from networks with low order eigenvectors"
  Abstract: We consider the problem of inferring meaningful spatial information in networks from incomplete information on the connection intensity between the nodes of the network. We consider two spatially distributed networks: a population migration flow network within the US, and a network of mobile phone calls between cities in Belgium. For both networks we use the eigenvectors of the Laplacian matrix constructed from the link intensities to obtain informative visualizations and capture natural geographical subdivisions. We observe that some low order eigenvectors localize very well and seem to reveal small geographically cohesive regions that match remarkably well with political and administrative boundaries. We discuss possible explanations for this observation by describing diffusion maps and localized eigenfunctions. In addition, we discuss a possible connection with the weighted graph cut problem, and provide numerical evidence supporting the idea that lower order eigenvectors point out local cuts in the network. However, we do not provide a formal and rigorous justification for our observations.

Connected through hyperedge HE-1 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-2 (induced by source paper "an mbo scheme for clustering and semi supervised clustering of signed networks"):
  with members:
    - "node classification for signed networks using diffuse interface methods"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a survey of signed network mining in social media"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "notes on elementary spectral graph theory applications to graph clustering using normalized cuts"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "max cut and the smallest eigenvalue"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "synchronization over z2 and community detection in signed multiplex networks with constraints"):
  with members:
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "localization on low order eigenvectors of data matrices"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-4 (induced by source paper "a method based on total variation for network modularity optimization using the mbo scheme"):
  with members:
    - "core periphery structure in networks"
    - "social structure of facebook networks"
    - "stable image reconstruction using total variation minimization"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "localization on low order eigenvectors of data matrices"):
  with members:
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "max cut and the smallest eigenvalue"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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)