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 "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"):
  Abstract: A novel approach termed  stochastic truncated amplitude flow  (STAF) is developed to reconstruct an unknown   $n$  -dimensional real-/complex-valued signal   $\boldsymbol {x}$   from    $m$   “phaseless” quadratic equations of the form   $\psi _i=|\langle \boldsymbol {a}_i,\boldsymbol {x}\rangle |$   . This problem, also known as phase retrieval from magnitude-only information, is  NP-hard   in general. Adopting an amplitude-based nonconvex formulation, STAF leads to an iterative solver comprising two stages: s1) Orthogonality-promoting initialization through a stochastic variance reduced gradient algorithm; and, s2) a series of iterative refinements of the initialization using stochastic truncated gradient iterations. Both stages involve a single equation per iteration, thus rendering STAF a simple, scalable, and fast approach amenable to large-scale implementations that are useful when   $n$    is large. When   $\lbrace \boldsymbol {a}_i\rbrace _{i=1}^m$    are independent Gaussian, STAF provably recovers exactly any    $\boldsymbol {x}\in \mathbb{R}^n$   exponentially fast based on order of   $n$   quadratic equations. STAF is also robust in the presence of additive noise of bounded support. Simulated tests involving real Gaussian    $\lbrace \boldsymbol {a}_i\rbrace$   vectors demonstrate that STAF empirically reconstructs any   $\boldsymbol {x}\in \mathbb{R}^n$    exactly from about   $2.3n$   magnitude-only measurements, outperforming state-of-the-art approaches and narrowing the gap from the information-theoretic number of equations   $m=2n-1$   . Extensive experiments using synthetic data and real images corroborate markedly improved performance of STAF over existing alternatives.

Includes members:
  Member "undersampled sparse phase retrieval via majorization minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "phase retrieval from binary measurements")
  Member "low rank phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "inexact alternating optimization for phase retrieval in the presence of outliers":
    also connected through other hyperedges:
      (none)
  Member "sparse phase retrieval via truncated amplitude flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "non convex phase retrieval from stft measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "phase retrieval using feasible point pursuit algorithms and cramer rao bound":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval from binary measurements")
  Member "phasemax convex phase retrieval via basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "guaranteed matrix completion via non convex factorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)