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: "distributed linear network operators using graph filters"
  Abstract: We study the design of graph filters to implement arbitrary linear transformations between graph signals. Graph filters can be represented by matrix polynomials of the graph-shift operator, which captures the structure of the graph and is assumed to be given. Thus, graph-filter design consists in choosing the coefficients of these polynomials (known as filter coefficients) to resemble desired linear transformations. Due to the local structure of the graph-shift operator, graph filters can be implemented distributedly across nodes, making them suitable for networked settings. We determine spectral conditions under which a specific linear transformation can be implemented perfectly using graph filters. Furthermore, for the cases where perfect implementation is infeasible, the design of optimal approximations for different error metrics is analyzed. We introduce the notion of a node-variant graph filter, which allows the simultaneous implementation of multiple (regular) graph filters in different nodes of the graph. This additional flexibility enables the design of more general operators without undermining the locality in implementation. Perfect and approximate implementation of network operators is also studied for node-variant graph filters. We demonstrate the practical relevance of the developed framework by studying in detail the application of graph filters to the problems of finite-time consensus and analog network coding. Finally, we present additional numerical experiments comparing the performance of node-invariant and node-variant filters when approximating arbitrary linear network operators.

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"
    - "network topology identification from spectral templates"
    - "subsampling for graph power spectrum estimation"
    - "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"
    - "network topology identification from spectral templates"
    - "self calibration and biconvex compressive sensing"
    - "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 "the dual graph shift operator identifying the support of the frequency domain"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "network topology inference from spectral templates"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "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-4 (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"
    - "network topology identification from spectral templates"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "learning sparse graphs under smoothness prior"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-5 (induced by source paper "reconstruction of graph signals through percolation from seeding nodes"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"
    - "active semi supervised learning using sampling theory for graph signals"
    - "identifiability scaling laws in bilinear inverse problems"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-6 (induced by source paper "sampling of graph signals with successive local aggregations"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs variation minimization"
    - "sampling large data on graphs"
    - "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-7 (induced by source paper "network topology identification from spectral templates"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "signal processing on graphs modeling causal relations in big data"
    - "learning laplacian matrix in smooth graph signal representations"
    - "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"

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.IT(Information Theory)