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: "on the stability of sparse convolutions"
  Abstract: We give a stability result for sparse convolutions on $\ell^2(G)\times \ell^1(G)$ for torsion-free discrete Abelian groups $G$ such as $\mathbb{Z}$. It turns out, that the torsion-free property prevents full cancellation in the convolution of sparse sequences and hence allows to establish stability in each entry, that is, for any fixed entry of the convolution the resulting linear map is injective with an universal lower norm bound, which only depends on the support cardinalities of the sequences. This can be seen as a reverse statement of the famous Young inequality for sparse convolutions. Our result hinges on a compression argument in additive set theory.

Connected through hyperedge HE-1 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "blind demixing and deconvolution at near optimal rate"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "explicit frames for deterministic phase retrieval via phaselift"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-2 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs"):
  with members:
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "compressed sensing on the image of bilinear maps"

Connected through hyperedge HE-3 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"):
  with members:
    - "identifiability scaling laws in bilinear inverse problems"
    - "ripless compressed sensing from anisotropic measurements"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "phase retrieval stability and recovery guarantees"

Connected through hyperedge HE-4 (induced by source paper "ofdm channel estimation via phase retrieval"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phase retrieval an overview of recent developments"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "phase retrieval for sparse signals"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "phase retrieval stability and recovery guarantees"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

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)