human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  Abstract: The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Includes members:
  Member "detectability thresholds of general modular graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "community detection and improved detectability in multiplex networks")
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "estimating the number of communities in a network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mean field theory of graph neural networks in graph partitioning")
  Member "identification of core periphery structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "link prediction in networks with core fringe data")
  Member "detectability of the spectral method for sparse graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "phase transitions in semisupervised clustering of sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "efficiently inferring community structure in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extracting hierarchical backbones from bipartite networks")

Question: Please tell me which class the source paper 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)