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 "a partial derandomization of phaselift using spherical designs"):
  Abstract: The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced algorithm for phase recovery that is computationally tractable, numerically stable, and comes with rigorous performance guarantees. PhaseLift is optimal in the sense that the number of amplitude measurements required for phase reconstruction scales linearly with the dimension of the signal. However, it specifically demands Gaussian random measurement vectors—a limitation that restricts practical utility and obscures the specific properties of measurement ensembles that enable phase retrieval. Here we present a partial derandomization of PhaseLift that only requires sampling from certain polynomial size vector configurations, called \(t\) -designs. Such configurations have been studied in algebraic combinatorics, coding theory, and quantum information. We prove reconstruction guarantees for a number of measurements that depends on the degree \(t\) of the design. If the degree is allowed to grow logarithmically with the dimension, the bounds become tight up to polylog-factors. Beyond the specific case of PhaseLift, this work highlights the utility of spherical designs for the derandomization of data recovery schemes.

Includes members:
  Member "improved recovery guarantees for phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "structured random measurements in signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "ripless compressed sensing from anisotropic measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "sparse signal recovery from quadratic measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval stability and recovery guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "consensus based optimization on the sphere ii convergence to global minimizers and machine learning")
  Member "solving quadratic equations via phaselift when there are about as many equations as unknowns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval with polarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hardness results for structured linear systems")

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)