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 "blind identification of stochastic block models from dynamical observations"):
  Abstract: We consider a blind identification problem in which we aim to recover a statistical model of a network without knowledge of the network's edges, but based solely on nodal observations of a certain process. More concretely, we focus on observations that consist of single snapshots taken from multiple trajectories of a diffusive process that evolves over the unknown network. We model the network as generated from an independent draw from a latent stochastic block model (SBM), and our goal is to infer both the partition of the nodes into blocks, as well as the parameters of this SBM. We discuss some non-identifiability issues related to this problem and present simple spectral algorithms that provably solve the partition recovery and parameter estimation problems with high accuracy. Our analysis relies on recent results in random matrix theory and covariance estimation, and associated concentration inequalities. We illustrate our results with several numerical experiments.

Includes members:
  Member "spectral partitioning of time varying networks with unobserved edges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind inference of centrality rankings from graph signals")
  Member "blind community detection from low rank excitations of a graph filter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "the sample complexity of multi reference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "centrality measures for graphons accounting for uncertainty in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphon pooling in graph neural networks")
  Member "network inference from consensus dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "active sensing of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)