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: "network topology identification from spectral templates"
  Abstract: Network topology inference is a cornerstone problem in statistical analyses of complex systems. In this context, the fresh look advocated here permeates benefits from convex optimization and graph signal processing, to identify the so-termed graph shift operator (encoding the network topology) given only the eigenvectors of the shift. These spectral templates can be obtained, for example, from principal component analysis of a set of graph signals defined on the particular network. The novel idea is to find a graph shift that while being consistent with the provided spectral information, it endows the network structure with certain desired properties such as sparsity. The focus is on developing efficient recovery algorithms along with identifiability conditions for two particular shifts, the adjacency matrix and the normalized graph Laplacian. Application domains include network topology identification from steady-state signals generated by a diffusion process, and design of a graph filter that facilitates the distributed implementation of a prescribed linear network operator. Numerical tests showcase the effectiveness of the proposed algorithms in recovering synthetic and structural brain networks.

Connected through hyperedge HE-1 (induced by source paper "stationary graph processes and spectral estimation"):
  with members:
    - "blind identification of graph filters"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "subsampling for graph power spectrum estimation"
    - "distributed linear network operators using graph filters"
    - "distributed autoregressive moving average graph filters"

Connected through hyperedge HE-2 (induced by source paper "blind identification of graph filters"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "self calibration and biconvex compressive sensing"
    - "distributed linear network operators using graph filters"
    - "a unified framework for identifiability analysis in bilinear inverse problems with applications to subspace and sparsity models"
    - "identifiability scaling laws in bilinear inverse problems"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

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

Connected through hyperedge HE-4 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "compressive spectral clustering"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"

Connected through hyperedge HE-5 (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"
    - "learning sparse graphs under smoothness prior"

Connected through hyperedge HE-6 (induced by source paper "network topology inference from spectral templates"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "learning heat diffusion graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "learning sparse graphs under smoothness prior"
    - "distributed linear network operators using graph filters"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-7 (induced by source paper "learning sparse graphs under smoothness prior"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "subsampling for graph power spectrum estimation"
    - "learning laplacian matrix in smooth graph signal representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of 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)