human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "phaseless compressive sensing using partial support information"):
  Abstract: We study the recovery conditions of weighted $\ell_1$ minimization for real-valued signal reconstruction from phaseless compressive sensing measurements when partial support information is available. A strong restricted isometry property condition is provided to ensure the stable recovery. Moreover, we present the weighted null space property as the sufficient and necessary condition for the success of $k$-sparse phaseless recovery via weighted $\ell_1$ minimization. Numerical experiments are conducted to illustrate our results.

Includes members:
  Member "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information":
    also connected through other hyperedges:
      (none)
  Member "recovery analysis for weighted mixed ell_2 ell_p minimization with 0 p leq 1":
    also connected through other hyperedges:
      (none)
  Member "recovery of signals under the high order rip condition via prior support information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information")
  Member "solving random quadratic systems of equations is nearly as easy as solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "phase retrieval via wirtinger flow theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "a strong restricted isometry property with an application to phaseless compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval")
  Member "phase retrieval for sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stable phaseless sampling and reconstruction of real valued signals with finite rate of innovations")
  Member "gespar efficient phase retrieval of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")

Question: Please tell me which class the source paper 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)