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: "self calibration via linear least squares"
  Abstract: Whenever we use devices to take measurements, calibration is indispensable. While#N#         the purpose of calibration is to reduce bias and uncertainty in the measurements, it can be#N#         quite difficult, expensive and sometimes even impossible to implement. We study a#N#         challenging problem called self-calibration, i.e., the task of designing an algorithm for#N#         devices so that the algorithm is able to perform calibration automatically. More precisely,#N#         we consider the setup $\boldsymbol{y} = \mathcal{A}(\boldsymbol{d}) \boldsymbol{x} +#N#         \boldsymbol{\epsilon}$ where only partial information about the sensing matrix#N#         $\mathcal{A}(\boldsymbol{d})$ is known and where $\mathcal{A}(\boldsymbol{d})$ linearly#N#         depends on $\boldsymbol{d}$. The goal is to estimate the calibration parameter#N#         $\boldsymbol{d}$ (resolve the uncertainty in the sensing process) and the signal/object of#N#         interests $\boldsymbol{x}$ simultaneously. For three different models of practical#N#         relevance we show how such a bilinear inverse problem, including blind deconvolution as an#N#         important example, can be solved via a simple linear least squares approach. As a#N#         consequence, the proposed algorithms are numerically extremely efficient, thus allowing for#N#         real-time deployment. Explicit theoretical guarantees and stability theory are derived and#N#         the number of sampling complexity is nearly optimal (up to a poly-log factor). Applications#N#         in imaging sciences and signal processing are discussed and numerical simulations are#N#         presented to demonstrate the effectiveness and efficiency of our approach.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "signal recovery in perturbed fourier compressed sensing"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and biconvex compressive sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"
    - "joint sparsity with different measurement matrices"

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)