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: "detectability of the spectral method for sparse graph partitioning"
  Abstract: We show that modularity maximization with the resolution parameter offers a unifying framework of graph partitioning. In this framework, we demonstrate that the spectral method exhibits universal detectability, irrespective of the value of the resolution parameter, as long as the graph is partitioned. Furthermore, we show that when the resolution parameter is sufficiently small, a first-order phase transition occurs, resulting in the graph being unpartitioned.

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"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "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"
    - "localized eigenvectors of the non backtracking matrix"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "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"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse 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)