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: "phaseless pca low rank matrix recovery from column wise phaseless measurements"
  Abstract: We study the following problem: recover a low-rank matrix from phaseless (magnitude-only) linear projections of each of its columns. In analogy with Robust PCA, we refer to this problem as "Phaseless PCA". It finds important applications in phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens. This work introduces the first provably correct solution, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), for this problem. Our guarantee for AltMinLowRaP shows that it can recover an $n \times q$ matrix of rank $r$ to $\epsilon$ accuracy using only about $nr^4 \log(1/\epsilon)$ measurements, as long as the matrix condition number is bounded by a numerical constant. This sample complexity is only $r^3$ times worse than the order-optimal value of $\max(n,q) r$. The only other assumption needed for our result is incoherence of the right singular vectors of the matrix. We demonstrate the practical advantage of AltMinLowRaP over existing work via extensive simulation, and some real-data, experiments. We also briefly study the dynamic extension of the above problem.

Connected through hyperedge HE-1 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "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"
    - "fast algorithms for robust pca via gradient descent"

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.LG(Machine Learning)