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 graph filters"):
  Abstract: Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of  linear graph filters  modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal   ${\mathbf y}$   modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients    ${\mathbf h}$  , and the input signal    ${\mathbf x}$   which is assumed to be sparse. While   ${\mathbf y}$   is a bilinear function of   ${\mathbf x}$   and    ${\mathbf h}$  , the filtered graph signal is also a linear combination of the entries of the lifted rank-one, row-sparse matrix    ${\mathbf x}{\mathbf h}^T$  . The blind graph-filter identification problem can thus be tackled via rank and sparsity minimization subject to linear constraints, an inverse problem amenable to convex relaxations offering provable recovery guarantees under simplifying assumptions. Numerical tests using both synthetic and real-world networks illustrate the merits of the proposed algorithms, as well as the benefits of leveraging multiple signals to aid the blind identification task.

Includes members:
  Member "reconstruction of graph signals through percolation from seeding nodes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "network topology identification from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stationary graph processes and spectral estimation")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "distributed linear network operators using graph filters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stationary graph processes and spectral estimation")
  Member "a unified framework for identifiability analysis in bilinear inverse problems with applications to subspace and sparsity models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind demixing and deconvolution at near optimal rate")
  Member "identifiability scaling laws in bilinear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture")
  Member "discrete signal processing on graphs frequency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edgenets edge varying graph neural networks")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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.IT(Information Theory)