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: "stable recovery from the magnitude of symmetrized fourier measurements"
  Abstract: In this note we show that stable recovery of complex-valued signals $x\in\mathbb{C}^n$ up to global sign can be achieved from the magnitudes of $4n-1$ Fourier measurements when a certain "symmetrization and zero-padding" is performed before measurement ($4n-3$ is possible in certain cases). For real signals, symmetrization itself is linear and therefore our result is in this case a statement on uniform phase retrieval. Since complex conjugation is involved, such measurement procedure is not complex-linear but recovery is still possible from magnitudes of linear measurements on, for example, $(\Re(x),\Im(x))$.

Connected through hyperedge HE-1 (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"
    - "on the stability of sparse convolutions"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "compressed sensing on the image of bilinear maps"

Connected through hyperedge HE-2 (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"
    - "on the stability of sparse convolutions"
    - "ripless compressed sensing from anisotropic 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-3 (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"
    - "on the stability of sparse convolutions"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the stability of sparse convolutions"):
  with members:
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "the restricted isometry property for time frequency structured random matrices"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-5 (induced by source paper "a stability result for sparse convolutions"):
  with members:
    - "compressed sensing on the image of bilinear maps"

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)