human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "provable low rank phase retrieval and compressive pca"):
  Abstract: We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix $X^*$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns. To be precise, we need to recover $X^*$ from $y_k := |A_k{}' x^*_k|, k=1,2,\dots, q$ when the measurement matrices $A_k$ are mutually independent. Here $y_k \in \mathbb{R}^m$. The question is when can we solve LRPR with $m \ll n$? Our work introduces the first provably correct solution, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), for solving LRPR. We demonstrate its advantage over existing work via extensive simulation, and some partly real data, experiments. Our guarantee for AltMinLowRaP shows that it can solve LRPR to $\epsilon$ accuracy if $m q \ge C n r^4 \log(1/\epsilon)$, the matrices $A_k$ contain i.i.d. standard Gaussian entries, the condition number of $X^*$ is bounded by a numerical constant, and its right singular vectors satisfy the incoherence (denseness) assumption from matrix completion literature. Its time complexity is only $ C mq nr \log^2(1/\epsilon)$. In the regime of small $r$, our sample complexity is much better than what standard PR methods need; and it is only about $r^3$ times worse than its order-optimal value of $(n + q) r$. Moreover, if we replace $m$ by its lower bound for each approach, then the same can be said for the time complexity comparison with standard PR. We also briefly study the dynamic extension of LRPR.  The LRPR problem occurs in phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens, where acquiring measurements is expensive. We should point out that LRPR is a very different problem than its $A_k =A$ version, or its $A_k = A$ and with-phase (linear) version, both of which have been extensively studied in the literature.

Includes members:
  Member "nonconvex optimization meets low rank matrix factorization an overview":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "provable subspace tracking from missing data and matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "phaseless pca low rank matrix recovery from column wise phaseless measurements":
    also connected through other hyperedges:
      (none)
  Member "provable dynamic robust pca or robust subspace tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "low rank phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "nearly optimal robust subspace tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "sparse phase retrieval via truncated amplitude flow":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors")

Question: Please tell me which class the source paper 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)