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 calibration in compressed sensing using message passing algorithms"
  Abstract: Compressed sensing (CS) is a concept that allows to acquire compressible signals with a small number of measurements. As such it is very attractive for hardware implementations. Therefore, correct calibration of the hardware is a central is- sue. In this paper we study the so-called blind calibration, i.e. when the training signals that are available to perform the calibration are sparse but unknown. We extend the approximate message passing (AMP) algorithm used in CS to the case of blind calibration. In the calibration-AMP, both the gains on the sensors and the elements of the signals are treated as unknowns. Our algorithm is also applica- ble to settings in which the sensors distort the measurements in other ways than multiplication by a gain, unlike previously suggested blind calibration algorithms based on convex relaxations. We study numerically the phase diagram of the blind calibration problem, and show that even in cases where convex relaxation is pos- sible, our algorithm requires a smaller number of measurements and/or signals in order to perform well.

Connected through hyperedge HE-1 (induced by source paper "self calibration methods for uncontrolled environments in sensor networks a reference survey"):
  with members:
    - "calibrating chemical multisensory devices for real world applications an in depth comparison of quantitative machine learning approaches"
    - "self calibration and biconvex compressive sensing"
    - "convex optimization approaches for blind sensor calibration using sparsity"

Connected through hyperedge HE-2 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

Connected through hyperedge HE-3 (induced by source paper "self calibration and biconvex compressive sensing"):
  with members:
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "exact joint sparse frequency recovery via optimization methods"
    - "identifiability scaling laws in bilinear inverse problems"
    - "a stability result for sparse convolutions"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "sparse signal recovery from quadratic measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "blind sensor calibration using approximate message passing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "on convergence of approximate message passing"
    - "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Connected through hyperedge HE-5 (induced by source paper "convex optimization approaches for blind sensor calibration using sparsity"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "balancing sparsity and rank constraints in quadratic basis pursuit"
    - "quadratic basis pursuit"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

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)