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: "spectral partitioning of time varying networks with unobserved edges"
  Abstract: We discuss a variant of ‘blind’ community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network. We consider a scenario where our observed graph signals are obtained by filtering white noise input, and the underlying network is different for every observation. In this fashion, the filtered graph signals can be interpreted as defined on a time-varying network. We model each of the underlying network realizations as generated by an independent draw from a latent stochastic blockmodel (SBM). To infer the partition of the latent SBM, we propose a simple spectral algorithm for which we provide a theoretical analysis and establish consistency guarantees for the recovery. We illustrate our results using numerical experiments on synthetic and real data, highlighting the efficacy of our approach.

Connected through hyperedge HE-1 (induced by source paper "blind identification of stochastic block models from dynamical observations"):
  with members:
    - "blind community detection from low rank excitations of a graph filter"
    - "the sample complexity of multi reference alignment"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "network inference from consensus dynamics"
    - "the ground truth about metadata and community detection in networks"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "reconstruction of directed networks from consensus dynamics"

Connected through hyperedge HE-2 (induced by source paper "blind inference of centrality rankings from graph signals"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "sampling on networks estimating eigenvector centrality on incomplete graphs"
    - "network inference from consensus dynamics with unknown parameters"
    - "blind identification of stochastic block models from dynamical observations"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "stability and continuity of centrality measures in weighted graphs"

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)