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: "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
  Abstract: We consider the problem of reconstructing two signals from the autocorrelation and cross-correlation measurements. This inverse problem is a fundamental one in signal processing, and arises in many applications, including phase retrieval and blind channel estimation. In a typical phase retrieval setup, only the autocorrelation measurements are obtainable. We show that, when the measurements are obtained using three simple "masks", phase retrieval reduces to the aforementioned reconstruction problem. #R##N#The classic solution to this problem is based on finding common factors between the $z$-transforms of the autocorrelation and cross-correlation vectors. This solution has enjoyed limited practical success, mainly due to the fact that it is not sufficiently stable in the noisy setting. In this work, inspired by the success of convex programming in provably and stably solving various quadratic constrained problems, we develop a semidefinite programming-based algorithm and provide theoretical guarantees. In particular, we show that almost all signals can be uniquely recovered by this algorithm (up to a global phase). Comparative numerical studies demonstrate that the proposed method significantly outperforms the classic method in the noisy setting.

Connected through hyperedge HE-1 (induced by source paper "noncoherent short packet communication via modulation on conjugated zeros"):
  with members:
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "short message communication and fir system identification using huffman sequences"
    - "sparse signal processing concepts for efficient 5g system design"
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-2 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on the stability of sparse convolutions"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-5 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "on the stability of sparse convolutions"
    - "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-6 (induced by source paper "short message communication and fir system identification using huffman sequences"):
  with members:
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "blind demixing and deconvolution at near optimal rate"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "sparse signal processing concepts for efficient 5g system design"
    - "blind deconvolution using convex programming"

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)