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: "incremental method for spectral clustering of increasing orders"
  Abstract: The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications the number of clusters or communities (say, $K$) is generally unknown a-priori. Consequently, the majority of the existing methods either choose $K$ heuristically or they repeat the clustering method with different choices of $K$ and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the $K$-th eigenpairs of the Laplacian matrix given a collection of all the $K-1$ smallest eigenpairs. Our proposed method adapts the Laplacian matrix such that the batch eigenvalue decomposition problem transforms into an efficient sequential leading eigenpair computation problem. As a practical application, we consider user-guided spectral clustering. Specifically, we demonstrate that users can utilize the proposed incremental method for effective eigenpair computation and determining the desired number of clusters based on multiple clustering metrics.

Connected through hyperedge HE-1 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-2 (induced by source paper "leveraging social signal to improve item recommendation for matrix factorization"):
  with members:
    - "incremental eigenpair computation for graph laplacian matrices theory and applications"
    - "name disambiguation in anonymized graphs using network embedding"
    - "nous construction and querying of dynamic knowledge graphs"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"

Connected through hyperedge HE-3 (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-4 (induced by source paper "name disambiguation in anonymized graphs using network embedding"):
  with members:
    - "bayesian non exhaustive classification a case study online name disambiguation using temporal record streams"
    - "nous construction and querying of dynamic knowledge graphs"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "feature selection for classification under anonymity constraint"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "bayesian non exhaustive classification a case study online name disambiguation using temporal record streams"):
  with members:
    - "nous construction and querying of dynamic knowledge graphs"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "feature selection for classification under anonymity constraint"
    - "bayesian nonexhaustive learning for online discovery and modeling of emerging classes"

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)