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: "universal phase transition in community detectability under a stochastic block model"
  Abstract: We prove the existence of an asymptotic phase transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. National Academy of Sciences. 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the inter-community edge connection probability $p$ grows. This phase transition separates a sub-critical regime of small $p$, where modularity-based community detection successfully identifies the communities, from a super-critical regime of large $p$ where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase transition threshold $p^*$ is equal to $\sqrt{p_1\cdot p_2}$, where $p_i~(i=1,2)$ is the within-community edge connection probability. Thus the phase transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase transition threshold we propose an empirical method for estimating this threshold from real-world data.

Connected through hyperedge HE-1 (induced by source paper "phase transitions and a model order selection criterion for spectral graph clustering"):
  with members:
    - "estimating the number of communities in a network"
    - "spectral detection in the censored block model"
    - "local set based graph signal reconstruction"
    - "incremental method for spectral clustering of increasing orders"
    - "signal recovery on graphs variation minimization"
    - "multi layer graph analysis for dynamic social networks"
    - "exact recovery in the stochastic block model"
    - "phase transitions in spectral community detection"

Connected through hyperedge HE-2 (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"
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "deep community detection"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social networks"

Connected through hyperedge HE-3 (induced by source paper "phase transitions in spectral community detection of large noisy networks"):
  with members:
    - "deep community detection"
    - "phase transitions in spectral community detection"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"
    - "discrete signal processing on graphs"
    - "graph spectra and the detectability of community structure in networks"
    - "geosocial graph based community detection"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "detectability of the spectral method for sparse graph partitioning"):
  with members:
    - "localized eigenvectors of the non backtracking matrix"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-5 (induced by source paper "phase transitions in spectral community detection"):
  with members:
    - "node removal vulnerability of the largest component of a network"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"
    - "discrete signal processing on graphs"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "graph spectra and the detectability of community structure in networks"
    - "geosocial graph based community detection"
    - "a tutorial on spectral clustering"

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)