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: "phase transitions in spectral community detection"
  Abstract: Consider a network consisting of two subnetworks (communities) connected by some external edges. Given the network topology, the community detection problem can be cast as a graph partitioning problem that aims to identify the external edges as the graph cut that separates these two subnetworks. In this paper, we consider a general model where two arbitrarily connected subnetworks are connected by random external edges. Using random matrix theory and concentration inequalities, we show that when one performs community detection via spectral clustering there exists an abrupt phase transition as a function of the random external edge connection probability. Specifically, the community detection performance transitions from almost perfect detectability to low detectability near some critical value of the random external edge connection probability. We derive upper and lower bounds on the critical value and show that the bounds are equal to each other when two subnetwork sizes are identical. Using simulated and experimental data we show how these bounds can be empirically estimated to validate the detection reliability of any discovered communities.

Connected through hyperedge HE-1 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "principled multilayer network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "nous construction and querying of dynamic knowledge graphs"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"

Connected through hyperedge HE-2 (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"
    - "universal phase transition in community detectability under a stochastic 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"

Connected through hyperedge HE-3 (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"
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "deep community detection"
    - "dynamic stochastic blockmodels for time evolving social networks"

Connected through hyperedge HE-4 (induced by source paper "amos an automated model order selection algorithm for spectral graph clustering"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "spectral detection in the censored block model"
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "the power of side information in subgraph detection"):
  with members:
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "graph based anomaly detection and description a survey"
    - "bayesian discovery of threat networks"
    - "defining and evaluating network communities based on ground truth"
    - "generalized optimization framework for graph based semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "information diffusion of topic propagation in social media"):
  with members:
    - "spreading processes in multilayer networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "multi layer graph analysis for dynamic social networks"
    - "diffusion of real time information in social physical networks"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-7 (induced by source paper "signal representations on graphs tools and applications"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "constrained adaptive sensing"
    - "signals on graphs uncertainty principle and sampling"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"

Connected through hyperedge HE-8 (induced by source paper "phase transitions in spectral community detection of large noisy networks"):
  with members:
    - "universal phase transition in community detectability under a stochastic block model"
    - "deep 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"

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)