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 and a model order selection criterion for spectral graph clustering"
  Abstract: One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. We propose automated model order selection (AMOS), a solution to the SGC model selection problem under a random interconnection model (RIM) using a novel selection criterion that is based on an asymptotic phase transition analysis. AMOS can more generally be applied to discovering hidden block diagonal structure in symmetric non-negative matrices. Numerical experiments on simulated graphs validate the phase transition analysis, and real-world network data is used to validate the performance of the proposed model selection procedure.

Connected through hyperedge HE-1 (induced by source paper "scalable spectral clustering using random binning features"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "random warping series a random features method for time series embedding"
    - "incremental eigenpair computation for graph laplacian matrices theory and applications"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "compressive spectral clustering"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (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"
    - "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"
    - "spectral detection in the censored block model"

Connected through hyperedge HE-3 (induced by source paper "node embedding via word embedding for network community discovery"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "multilayer spectral graph clustering via convex layer aggregation"):
  with members:
    - "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"
    - "multi layer graph analysis for dynamic social networks"

Connected through hyperedge HE-5 (induced by source paper "amos an automated model order selection algorithm for spectral graph clustering"):
  with members:
    - "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"
    - "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"

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)