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 "low rank positive semidefinite matrix recovery from quadratic measurements with outliers"):
  Abstract: We address the problem of estimating a low-rank positive semidefinite (PSD) matrix $\boldsymbol{X}\in\mathbb{R}^{n\times n}$ from a set of measurements $z_i= \boldsymbol{a}_i^T\boldsymbol{X}\boldsymbol{a}_i$, $i=1,\ldots, m$, which are quadratic in the sensing vectors $\boldsymbol{a}_i$'s composed of i.i.d. standard Gaussian entries. This problem arises from applications such as covariance sketching, quantum space tomography, and power spectrum estimation. Specifically, we consider a convex optimization problem that seeks the PSD matrix with minimum $\ell_1$ norm of the observation residual. The advantage of our algorithm is that it is free of parameters, therefore eliminating the need for tuning parameters and allowing easy implementations. We establish that with high probability, a rank-r PSD matrix $\boldsymbol{X}$ of size-n can be exactly recovered from $\mathcal{O}(nr^{2})$ measurements, even when a fraction of the measurements is corrupted by outliers with arbitrary magnitudes. Moreover, the recovery is also stable against bounded noise. With the additional information of the rank of the PSD matrix, a non-convex algorithm based on subgradient descent is proposed that demonstrates superior empirical performance.

Includes members:
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "solving random quadratic systems of equations is nearly as easy as solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "phase retrieval via wirtinger flow theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "exact and stable covariance estimation from quadratic sampling via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "compressive principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "phaselift exact and stable signal recovery from magnitude measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "compressed sensing and matrix completion with constant proportion of corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")

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)