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: "accurate community detection in the stochastic block model via spectral algorithms"
  Abstract: We consider the problem of community detection in the Stochastic Block Model with a finite number $K$ of communities of sizes linearly growing with the network size $n$. This model consists in a random graph such that each pair of vertices is connected independently with probability $p$ within communities and $q$ across communities. One observes a realization of this random graph, and the objective is to reconstruct the communities from this observation. We show that under spectral algorithms, the number of misclassified vertices does not exceed $s$ with high probability as $n$ grows large, whenever $pn=\omega(1)$, $s=o(n)$ and \begin{equation*} \lim\inf_{n\to\infty} {n(\alpha_1 p+\alpha_2 q-(\alpha_1 + \alpha_2)p^{\frac{\alpha_1}{\alpha_1 + \alpha_2}}q^{\frac{\alpha_2}{\alpha_1 + \alpha_2}})\over \log (\frac{n}{s})} >1,\quad\quad(1) \end{equation*} where $\alpha_1$ and $\alpha_2$ denote the (fixed) proportions of vertices in the two smallest communities. In view of recent work by Abbe et al. and Mossel et al., this establishes that the proposed spectral algorithms are able to exactly recover communities whenever this is at all possible in the case of networks with two communities with equal sizes. We conjecture that condition (1) is actually necessary to obtain less than $s$ misclassified vertices asymptotically, which would establish the optimality of spectral method in more general scenarios.

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"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "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 "searching for a single community in a graph"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "estimating the number of communities in a network"
    - "exact recovery in the stochastic block model"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "breaking the small cluster barrier of graph clustering"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "multisection in the stochastic block model using semidefinite programming"):
  with members:
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "constant factor approximation for balanced cut in the pie model"
    - "exact recovery in the stochastic block model"
    - "multireference alignment using semidefinite programming"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-4 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "community recovery in graphs with locality"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "asymptotic mutual information for the two groups stochastic block model"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "near optimal joint object matching via convex relaxation"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "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.SI(Social and Information Networks)