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: "deep community detection"
  Abstract: A deep community in a graph is a connected component that can only be seen after removal of nodes or edges from the rest of the graph. This paper formulates the problem of detecting deep communities as multi-stage node removal that maximizes a new centrality measure, called the local Fiedler vector centrality (LFVC), at each stage. The LFVC is associated with the sensitivity of algebraic connectivity to node or edge removals. We prove that a greedy node/edge removal strategy, based on successive maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. Under a stochastic block model framework, we show that the greedy LFVC strategy can extract deep communities with probability one as the number of observations becomes large. We apply the greedy LFVC strategy to real-world social network datasets. Compared with conventional community detection methods we demonstrate improved ability to identify important communities and key members in the network.

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 "can gan learn topological features of a graph"):
  with members:
    - "nips 2016 tutorial generative adversarial networks"
    - "energy based generative adversarial network"
    - "semi supervised classification with graph convolutional networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional networks on graph structured data"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "hidden community detection in social networks"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "revealing multiple layers of hidden community structure in networks"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "social structure of facebook networks"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (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"
    - "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 "steering macro scale network community structure by micro scale features"):
  with members:
    - "discrete signal processing on graphs sampling theory"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "incremental method for spectral clustering of increasing orders"):
  with members:
    - "phase transitions in spectral community detection"
    - "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"
    - "a model based approach to rounding in spectral clustering"
    - "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)