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 "matrix recovery from rank one projection measurements via nonconvex minimization"):
  Abstract: In this paper, we consider the matrix recovery from rank-one projection measurements proposed in [Cai and Zhang, Ann. Statist., 43(2015), 102-138], via nonconvex minimization. We establish a sufficient identifiability condition, which can guarantee the exact recovery of low-rank matrix via Schatten-$p$ minimization $\min_{X}\|X\|_{S_p}^p$ for $0<p<1$ under affine constraint, and stable recovery of low-rank matrix under $\ell_q$ constraint and Dantzig selector constraint. Our condition is also sufficient to guarantee low-rank matrix recovery via least $q$ minimization $\min_{X}\|\mathcal{A}(X)-b\|_{q}^q$ for $0<q\leq1$. And we also extend our result to Gaussian design distribution, and show that any matrix can be stably recovered for rank-one projection from Gaussian distributions via least $1$ minimization with high probability.

Includes members:
  Member "low rank positive semidefinite matrix recovery from corrupted rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "exact and stable covariance estimation from quadratic sampling via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "compressive multiplexing of correlated signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "sparse signal recovery from quadratic measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "solving quadratic equations via phaselift when there are about as many equations as unknowns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phaselift exact and stable signal recovery from magnitude measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")

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.IT(Information Theory)