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: "cross validation estimate of the number of clusters in a network"
  Abstract: Network science investigates methodologies that summarise relational data to obtain better interpretability. Identifying modular structures is a fundamental task, and assessment of the coarse-grain level is its crucial step. Here, we propose principled, scalable, and widely applicable assessment criteria to determine the number of clusters in modular networks based on the leave-one-out cross-validation estimate of the edge prediction error.

Connected through hyperedge HE-1 (induced by source paper "democratic summary of public opinions in free response surveys"):
  with members:
    - "network structure from rich but noisy data"
    - "comparative analysis on the selection of number of clusters in community detection"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "algorithmic detectability threshold of the stochastic block model"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "algorithmic infeasibility of community detection in higher order networks"
    - "estimating the number of communities in a network"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-2 (induced by source paper "comparative analysis on the selection of number of clusters in community detection"):
  with members:
    - "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"
    - "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-3 (induced by source paper "simplicial closure and higher order link prediction"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "motifs in temporal networks"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "representing higher order dependencies in networks"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "supervised random walks predicting and recommending links in social 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)