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 conditions for uniqueness in sparse phase retrieval"
  Abstract: The phase retrieval problem has a long history and is an important problem in many areas of optics. Theoretical understanding of phase retrieval is still limited and fundamental questions such as uniqueness and stability of the recovered solution are not yet fully understood. This paper provides several additions to the theoretical understanding of sparse phase retrieval. In particular we show that if the measurement ensemble can be chosen freely, as few as 4k-1 phaseless measurements suffice to guarantee uniqueness of a k-sparse M-dimensional real solution. We also prove that 2(k^2-k+1) Fourier magnitude measurements are sufficient under rather general conditions.

Connected through hyperedge HE-1 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem"):
  with members:
    - "a note on douglas rachford subgradients and phase retrieval"
    - "phase retrieval for binary signals box relaxation and uniqueness"
    - "learning without loss"
    - "single particle cryo electron microscopy mathematical theory computational challenges and opportunities"
    - "blind phaseless short time fourier transform recovery"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"

Connected through hyperedge HE-2 (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"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "reconstruction of binary functions and shapes from incomplete frequency information"

Connected through hyperedge HE-3 (induced by source paper "on fienup methods for sparse phase retrieval"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"
    - "phase retrieval via matrix completion"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval with background information"):
  with members:
    - "sparse phase retrieval from short time fourier measurements"
    - "phase retrieval for sparse signals uniqueness conditions"

Connected through hyperedge HE-5 (induced by source paper "stft phase retrieval uniqueness guarantees and recovery algorithms"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval an overview of recent developments"
    - "sparse phase retrieval from short time fourier measurements"
    - "quadratic basis pursuit"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-6 (induced by source paper "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"):
  with members:
    - "on the 2d phase retrieval problem"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phasemax convex phase retrieval via basis pursuit"
    - "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"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"

Connected through hyperedge HE-7 (induced by source paper "on fast decoding of high dimensional signals from one bit measurements"):
  with members:
    - "phase retrieval an overview of recent developments"
    - "fast compressive phase retrieval from fourier measurements"
    - "for all sparse recovery in near optimal time"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "new conditions for sparse phase retrieval"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"

Connected through hyperedge HE-8 (induced by source paper "phase retrieval an overview of recent developments"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "sparse phase retrieval from short time fourier measurements"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "near optimal phase retrieval of sparse vectors"
    - "phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval convex algorithms and limitations"

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)