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: "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
  Abstract: In this paper, we present and analyze a simple and robust spectral algorithm for the stochastic block model with $k$ blocks, for any $k$ fixed. Our algorithm works with graphs having constant edge density, under an optimal condition on the gap between the density inside a block and the density between the blocks. As a co-product, we settle an open question posed by Abbe et. al. concerning censor block models.

Connected through hyperedge HE-1 (induced by source paper "randomized spectral clustering in large scale stochastic block models"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "graph sparsification by effective resistances"
    - "a tutorial on spectral clustering"
    - "faster least squares approximation"

Connected through hyperedge HE-2 (induced by source paper "efficient distributed community detection in the stochastic block model"):
  with members:
    - "large scale distributed algorithms for facility location with outliers"
    - "local mixing time distributed computation and applications"
    - "near optimal clustering in the k machine model"
    - "average whenever you meet opportunistic protocols for community detection"
    - "on the distributed complexity of large scale graph computations"
    - "find your place simple distributed algorithms for community detection"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-3 (induced by source paper "theory of spectral method for union of subspaces based random geometry graph"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "consistent community detection in continuous time networks of relational events"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the block point process model for continuous time event based dynamic networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "stochastic block transition models for dynamic networks"
    - "modeling events with cascades of poisson processes"

Connected through hyperedge HE-5 (induced by source paper "latent channel networks"):
  with members:
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "julia a fresh approach to numerical computing"
    - "exact recovery in the stochastic block model"
    - "social structure of facebook networks"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-6 (induced by source paper "adaptive path interpolation for sparse systems application to a simple censored block model"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "entropy and mutual information in models of deep neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "information theoretic thresholds from the cavity method"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"

Connected through hyperedge HE-7 (induced by source paper "graph powering and spectral robustness"):
  with members:
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "the geometric block model"
    - "robust estimators in high dimensions without the computational intractability"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "spectral redemption in clustering sparse networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "on the minimax misclassification ratio of hypergraph community detection"):
  with members:
    - "community recovery in hypergraphs"
    - "motif clustering and overlapping clustering for social network analysis"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "spectral detection on sparse hypergraphs"
    - "exact recovery in the stochastic block model"

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.DS(Data Structures and Algorithms)