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: "phase retrieval for sparse signals"
  Abstract: The aim of this paper is to build up the theoretical framework for the recovery of sparse signals from the magnitude of the measurement. We first investigate the minimal number of measurements for the success of the recovery of sparse signals without the phase information. We completely settle the minimality question for the real case and give a lower bound for the complex case. We then study the recovery performance of the $\ell_1$ minimization. In particular, we present the null space property which, to our knowledge, is the first sufficient and necessary condition for the success of $\ell_1$ minimization for $k$-sparse phase retrievable.

Connected through hyperedge HE-1 (induced by source paper "stable phaseless sampling and reconstruction of real valued signals with finite rate of innovations"):
  with members:
    - "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"
    - "phase retrieval of real valued signals in a shift invariant space"
    - "spatially distributed sampling and reconstruction"
    - "phase retrieval an overview of recent developments"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-2 (induced by source paper "phaseless compressive sensing using partial support information"):
  with members:
    - "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"
    - "recovery analysis for weighted mixed ell_2 ell_p minimization with 0 p leq 1"
    - "recovery of signals under the high order rip condition via prior support information"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "the minimal measurement number problem in phase retrieval a review of recent developments"):
  with members:
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "non convex phase retrieval from stft measurements"):
  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"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"

Connected through hyperedge HE-5 (induced by source paper "phase retrieval of real valued signals in a shift invariant space"):
  with members:
    - "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "prime phase retrieval via majorization minimization"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "spatially distributed sampling and reconstruction"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"

Connected through hyperedge HE-6 (induced by source paper "phaseless rcovery using gauss newton method"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "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"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-7 (induced by source paper "phase retrieval from the magnitudes of affine linear measurements"):
  with members:
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-8 (induced by source paper "generalized phase retrieval measurement number matrix recovery and beyond"):
  with members:
    - "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)