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 arbitrary expected degrees"
  Abstract: We study random graphs with arbitrary distributions of expected degree and derive expressions for the spectra of their adjacency and modularity matrices. We give a complete prescription for calculating the spectra that is exact in the limit of large network size and large vertex degrees. We also study the effect on the spectra of hubs in the network, vertices of unusually high degree, and show that these produce isolated eigenvalues outside the main spectral band, akin to impurity states in condensed matter systems, with accompanying eigenvectors that are strongly localized around the hubs. We give numerical results that confirm our analytic expressions.

Connected through hyperedge HE-1 (induced by source paper "message passing on networks with loops"):
  with members:
    - "spectra of networks containing short loops"

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"
    - "spectra of random graphs with community structure and arbitrary degrees"

Connected through hyperedge HE-3 (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"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-4 (induced by source paper "controllability gramian spectra of random networks"):
  with members:
    - "controllability and fraction of leaders in infinite networks"
    - "structural controllability of multi agent networks robustness against simultaneous failures"
    - "controllability metrics limitations and algorithms for complex networks"
    - "observability with random observations"

Connected through hyperedge HE-5 (induced by source paper "a spectral framework for anomalous subgraph detection"):
  with members:
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "bayesian discovery of threat networks"
    - "graph spectra and the detectability of community structure in networks"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "reinventing the triangles rule of thumb for assessing detectability"):
  with members:
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "graph spectra and the detectability of community structure in networks"
    - "approximate triangle counting"

Connected through hyperedge HE-7 (induced by source paper "localization and centrality in networks"):
  with members:
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "spectral properties of google matrix of wikipedia and other networks"
    - "spectral redemption in clustering sparse 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)