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 strong restricted isometry property with an application to phaseless compressed sensing"
  Abstract: The many variants of the restricted isometry property (RIP) have proven to be crucial theoretical tools in the fields of compressed sensing and matrix completion. The study of extending compressed sensing to accommodate phaseless measurements naturally motivates a strong notion of restricted isometry property (SRIP), which we develop in this paper. We show that if $A \in \mathbb{R}^{m\times n}$ satisfies SRIP and phaseless measurements $|Ax_0| = b$ are observed about a $k$-sparse signal $x_0 \in \mathbb{R}^n$, then minimizing the $\ell_1$ norm subject to $ |Ax| = b $ recovers $x_0$ up to multiplication by a global sign. Moreover, we establish that the SRIP holds for the random Gaussian matrices typically used for standard compressed sensing, implying that phaseless compressed sensing is possible from $O(k \log (n/k))$ measurements with these matrices via $\ell_1$ minimization over $|Ax| = b$. Our analysis also yields an erasure robust version of the Johnson-Lindenstrauss Lemma.

Connected through hyperedge HE-1 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval"):
  with members:
    - "phase retrieval from the norms of affine transformations"
    - "sparse phase retrieval via truncated amplitude flow"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-2 (induced by source paper "phaseless compressive sensing using partial support information"):
  with members:
    - "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"
    - "recovery analysis for weighted mixed ell_2 ell_p minimization with 0 p leq 1"
    - "recovery of signals under the high order rip condition via prior support information"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval for sparse signals"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "undersampled sparse phase retrieval via majorization minimization"):
  with members:
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "prime phase retrieval via majorization minimization"
    - "compressive phase retrieval via generalized approximate message passing"
    - "sparse phase retrieval from short time fourier measurements"
    - "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 "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

Connected through hyperedge HE-5 (induced by source paper "phaseless rcovery using gauss newton method"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval for sparse signals"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "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 "robustness properties of dimensionality reduction with gaussian random matrices"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "random matrices and erasure robust frames"
    - "phase retrieval for sparse signals"
    - "near optimal phase retrieval of sparse vectors"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

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)