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: "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.

Connected through hyperedge HE-1 (induced by source paper "compressive phase retrieval via reweighted amplitude flow"):
  with members:
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "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"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "low rank phase retrieval"):
  with members:
    - "correlated pca principal components analysis when data and noise are correlated"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval with application to optical imaging"
    - "fast matrix completion without the condition number"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval stability and recovery guarantees"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-3 (induced by source paper "undersampled sparse phase retrieval via majorization minimization"):
  with members:
    - "prime phase retrieval via majorization minimization"
    - "compressive phase retrieval via generalized approximate message passing"
    - "sparse phase retrieval from short time fourier measurements"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "sparse modeling for image and vision processing"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval via reweighted wirtinger flow"):
  with members:
    - "fourier ptychographic reconstruction using poisson maximum likelihood and truncated wirtinger gradient"
    - "a geometric analysis of phase retrieval"
    - "phaseless rcovery using gauss newton method"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-5 (induced by source paper "inexact alternating optimization for phase retrieval in the presence of outliers"):
  with members:
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "parallel matrix factorization for low rank tensor completion"
    - "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-6 (induced by source paper "phase retrieval via sparse wirtinger flow"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "phaseless rcovery using gauss newton method"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-7 (induced by source paper "sparse phase retrieval via truncated amplitude flow"):
  with members:
    - "channels with cooperation links that may be absent"
    - "sublinear time algorithms for compressive phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "undersampled sparse phase retrieval via majorization minimization"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "non convex phase retrieval from stft measurements"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"

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)