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 "information theoretic limits for community detection in network models"):
  Abstract: We analyze the information-theoretic limits for the recovery of node labels in several network models, including the stochastic block model, as well as the latent space model. For the stochastic block model, the non-recoverability condition depends on the probabilities of having edges inside a community, and between different communities. For the latent space model, the non-recoverability condition depends on the dimension of the latent space, and how far and spread are the communities in the latent space. We also extend our analysis to dynamic models in which edges not only depend on their endpoints, but also on previously generated edges.

Includes members:
  Member "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "community detection in the labelled stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "efficient and principled method for detecting communities in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exact blind community detection from signals on multiple graphs")

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.LG(Machine Learning)