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: "sparse phase retrieval convex algorithms and limitations"
  Abstract: We consider the problem of recovering signals from their power spectral density. This is a classical problem referred to in literature as the phase retrieval problem, and is of paramount importance in many fields of applied sciences. In general, additional prior information about the signal is required to guarantee unique recovery as the mapping from signals to power spectral density is not one-to-one. In this paper, we assume that the underlying signals are sparse. Recently, semidefinite programming (SDP) based approaches were explored by various researchers. Simulations of these algorithms strongly suggest that signals upto $o(\sqrt{n})$ sparsity can be recovered by this technique. In this work, we develop a tractable algorithm based on reweighted $l_1$-minimization that recovers a sparse signal from its power spectral density for significantly higher sparsities, which is unprecedented. #R##N#We discuss the square-root bottleneck of the existing convex algorithms and show that a $k$-sparse signal can be efficiently recovered using $O(k^2logn)$ phaseless Fourier measurements. We also show that a $k$-sparse signal can be recovered using only $O(k log n)$ phaseless measurements if we are allowed to design the measurement matrices.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of waveform channels under square law detection of time limited signals"):
  with members:
    - "information capacity of direct detection optical transmission systems"

Connected through hyperedge HE-2 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval for binary signals box relaxation and uniqueness"):
  with members:
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "reconstruction of binary functions and shapes from incomplete frequency information"

Connected through hyperedge HE-4 (induced by source paper "optimal spectral initialization for signal recovery with applications to phase retrieval"):
  with members:
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "a precise analysis of phasemax in phase retrieval"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "phasemax convex phase retrieval via basis pursuit"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-6 (induced by source paper "phase retrieval under a generative prior"):
  with members:
    - "sparse phase retrieval via truncated amplitude flow"
    - "a geometric analysis of phase retrieval"
    - "why are deep nets reversible a simple theory with implications for training"
    - "efficient compressive phase retrieval with constrained sensing vectors"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"
    - "sparse signal recovery from quadratic measurements via convex programming"

Connected through hyperedge HE-7 (induced by source paper "bispectrum inversion with application to multireference alignment"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "the sample complexity of multi reference alignment"
    - "non convex phase retrieval from stft measurements"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "fundamental limits in multi image alignment"

Connected through hyperedge HE-8 (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"
    - "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"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

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)