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: "blind sensor calibration using approximate message passing"
  Abstract: The ubiquity of approximately sparse data has led a variety of communities to take great interest in compressed sensing algorithms. Although these are very successful and well understood for linear measurements with additive noise, applying them to real data can be problematic if imperfect sensing devices introduce deviations from this ideal signal acquisition process, caused by sensor decalibration or failure. We propose a message passing algorithm called calibration approximate message passing (Cal-AMP) that can treat a variety of such sensor-induced imperfections. In addition to deriving the general form of the algorithm, we numerically investigate two particular settings. In the first, a fraction of the sensors is faulty, giving readings unrelated to the signal. In the second, sensors are decalibrated and each one introduces a different multiplicative gain to the measurements. Cal-AMP shares the scalability of approximate message passing, allowing us to treat large sized instances of these problems, and experimentally exhibits a phase transition between domains of success and failure.

Connected through hyperedge HE-1 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "low complexity multiclass encryption by compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "through the haze a non convex approach to blind gain calibration for linear random sensing models"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"

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)