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: "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
  Abstract: We consider faithfully combining phase retrieval with classical compressed sensing. Inspired by the recent novel formulation for phase retrieval called PhaseMax, we present and analyze SparsePhaseMax, a linear program for phaseless compressed sensing in the natural parameter space. We establish that when provided with an initialization that correlates with an arbitrary $k$-sparse $n$-vector, SparsePhaseMax recovers this vector up to global sign with high probability from $O(k \log \frac{n}{k})$ magnitude measurements against i.i.d. Gaussian random vectors. Our proof of this fact exploits a curious newfound connection between phaseless and 1-bit compressed sensing. This is the first result to establish bootstrapped compressed sensing from phaseless Gaussian measurements under optimal sample complexity.

Connected through hyperedge HE-1 (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"
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "solving equations of random convex functions via anchored regression"):
  with members:
    - "learning relus via gradient descent"
    - "corruption robust phase retrieval via linear programming"
    - "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"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-3 (induced by source paper "compressive phase retrieval via reweighted amplitude flow"):
  with members:
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "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"
    - "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-4 (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"
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "phasemax convex phase retrieval via basis pursuit"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase retrieval via reweighted wirtinger flow"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "non convex phase retrieval from stft measurements"
    - "corruption robust phase retrieval via linear programming"

Connected through hyperedge HE-6 (induced by source paper "phase retrieval meets statistical learning theory a flexible convex relaxation"):
  with members:
    - "corruption robust phase retrieval via linear programming"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval an overview of recent developments"

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)