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: "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
  Abstract: Investigating the performance of different methods is a fundamental problem in graph partitioning. In this paper, we estimate the so-called detectability threshold for the spectral method with both unnormalized and normalized Laplacians in sparse graphs. The detectability threshold is the critical point at which the result of the spectral method is completely uncorrelated to the planted partition. We also analyze whether the localization of eigenvectors affects the partitioning performance in the detectable region. We use the replica method, which is often used in the field of spin-glass theory, and focus on the case of bisection. We show that the gap between the estimated threshold for the spectral method and the threshold obtained from Bayesian inference is considerable in sparse graphs, even without eigenvector localization. This gap closes in a dense limit.

Connected through hyperedge HE-1 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure"):
  with members:
    - "graph powering and spectral robustness"
    - "robustness of spectral methods for community detection"
    - "detectability of the spectral method for sparse graph partitioning"
    - "spectral redemption in clustering sparse networks"
    - "graph spectra and the detectability of community structure in networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "mean field theory of graph neural networks in graph partitioning"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "algorithmic detectability threshold of the stochastic block model"
    - "deriving neural architectures from sequence and graph kernels"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "neural message passing for quantum chemistry"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-3 (induced by source paper "comparative analysis on the selection of number of clusters in community detection"):
  with members:
    - "cross validation estimate of the number of clusters in a network"
    - "the ground truth about metadata and community detection in networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "localized eigenvectors of the non backtracking matrix"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-4 (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "structure and inference in annotated networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-5 (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"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "spectral redemption in clustering sparse networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "a tractable fully bayesian method for the stochastic block model"):
  with members:
    - "rebuilding factorized information criterion asymptotically accurate marginal likelihood"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-7 (induced by source paper "detectability of the spectral method for sparse graph partitioning"):
  with members:
    - "universal phase transition in community detectability under a stochastic block model"
    - "localized eigenvectors of the non backtracking matrix"
    - "graph spectra and the detectability of community structure in 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)