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: "reconstruction of directed networks from consensus dynamics"
  Abstract: This paper addresses the problem of identifying the topology of an unknown, weighted, directed network running a consensus dynamics. We propose a methodology to reconstruct the network topology from the dynamic response when the system is stimulated by a wide-sense stationary noise of unknown power spectral density. The method is based on a node-knockout, or grounding, procedure wherein the grounded node broadcasts zero without being eliminated from the network. In this direction, we measure the empirical cross-power spectral densities of the outputs between every pair of nodes for both grounded and ungrounded consensus to reconstruct the unknown topology of the network. We also establish that in the special cases of undirected or purely unidirectional networks, the reconstruction does not need grounding. Finally, we extend our results to the case of a directed network assuming a general dynamics, and prove that the developed method can detect edges and their direction.

Connected through hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs"):
  with members:
    - "network inference from consensus dynamics with unknown parameters"
    - "blind inference of centrality rankings from graph signals"
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "blind identification of graph filters"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-2 (induced by source paper "blind identification of stochastic block models from dynamical observations"):
  with members:
    - "spectral partitioning of time varying networks with unobserved edges"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "network inference from consensus dynamics"):
  with members:
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "network structure inference a survey motivations methods and applications"
    - "topology identification of directed dynamical networks via power spectral analysis"

Connected through hyperedge HE-4 (induced by source paper "characterization and inference of graph diffusion processes from observations of stationary signals"):
  with members:
    - "graph learning under sparsity priors"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "network topology identification from spectral templates"

Connected through hyperedge HE-5 (induced by source paper "topology identification of directed dynamical networks via power spectral analysis"):
  with members:
    - "network reconstruction from intrinsic noise"

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)