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: "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.

Connected through hyperedge HE-1 (induced by source paper "complex phase retrieval from subgaussian measurements"):
  with members:
    - "on the convex geometry of blind deconvolution and matrix completion"
    - "phase retrieval without small ball probability assumptions"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "stable low rank matrix recovery via null space properties"

Connected through hyperedge HE-2 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"):
  with members:
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "analyzing the structure of multidimensional compressed sensing problems through coherence"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "ripless compressed sensing from anisotropic measurements"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "derandomizing compressed sensing with combinatorial design"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "low rank matrix recovery from clifford orbits"
    - "stable low rank matrix recovery via null space properties"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "a derandomized sparse johnson lindenstrauss transform"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval without small ball probability assumptions"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "phase retrieval with application to optical imaging"
    - "low rank matrix recovery from rank one measurements"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "one bit compressed sensing with non gaussian measurements"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-5 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-6 (induced by source paper "improved recovery guarantees for phase retrieval from coded diffraction patterns"):
  with members:
    - "low rank matrix recovery from rank one measurements"
    - "ripless compressed sensing from anisotropic measurements"
    - "phase retrieval from low rate samples"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-7 (induced by source paper "improving compressed sensing with the diamond norm"):
  with members:
    - "comments on improving compressed sensing with the diamond norm saturation of the norm inequalities between diamond and nuclear norm"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "stable low rank matrix recovery via null space properties"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"

Connected through hyperedge HE-8 (induced by source paper "explicit frames for deterministic phase retrieval via phaselift"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

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)