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: "spectra of random graphs with community structure and arbitrary degrees"
  Abstract: Using methods from random matrix theory researchers have recently calculated the full spectra of random networks with arbitrary degrees and with community structure. Both reveal interesting spectral features, including deviations from the Wigner semicircle distribution and phase transitions in the spectra of community structured networks. In this paper we generalize both calculations, giving a prescription for calculating the spectrum of a network with both community structure and an arbitrary degree distribution. In general the spectrum has two parts, a continuous spectral band, which can depart strongly from the classic semicircle form, and a set of outlying eigenvalues that indicate the presence of communities.

Connected through hyperedge HE-1 (induced by source paper "community detectability and structural balance dynamics in signed networks"):
  with members:
    - "balance in signed networks"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-2 (induced by source paper "comparative analysis on the selection of number of clusters in community detection"):
  with members:
    - "cross validation estimate of the number of clusters in a network"
    - "the ground truth about metadata and community detection in networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "localized eigenvectors of the non backtracking matrix"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "structured networks and coarse grained descriptions a dynamical perspective"):
  with members:
    - "multiscale dynamical embeddings of complex networks"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spectral redemption in clustering sparse networks"
    - "opinion fluctuations and disagreement in social networks"

Connected through hyperedge HE-4 (induced by source paper "a survey on theoretical advances of community detection in networks"):
  with members:
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "structure and inference in annotated networks"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "spectra of random graphs with arbitrary expected degrees"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-5 (induced by source paper "reinventing the triangles rule of thumb for assessing detectability"):
  with members:
    - "spectra of random graphs with arbitrary expected degrees"
    - "graph spectra and the detectability of community structure in networks"
    - "approximate triangle counting"

Connected through hyperedge HE-6 (induced by source paper "spectral anomaly detection in very large graphs models noise and computational complexity"):
  with members:
    - "a spectral framework for anomalous subgraph detection"

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)