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 and biconvex compressive sensing"
  Abstract: The design of high-precision sensing devises becomes ever more difficult and expensive. At the same time, the need for precise calibration of these devices (ranging from tiny sensors to space telescopes) manifests itself as a major roadblock in many scientific and technological endeavors. To achieve optimal performance of advanced high-performance sensors one must carefully calibrate them, which is often difficult or even impossible to do in practice. In this work we bring together three seemingly unrelated concepts, namely self-calibration, compressive sensing, and biconvex optimization. The idea behind self-calibration is to equip a hardware device with a smart algorithm that can compensate automatically for the lack of calibration. We show how several self-calibration problems can be treated efficiently within the framework of biconvex compressive sensing via a new method called SparseLift. More specifically, we consider a linear system of equations where both and the diagonal matrix (which models the calibration error) are unknown. By 'lifting' this biconvex inverse problem we arrive at a convex optimization problem. By exploiting sparsity in the signal model, we derive explicit theoretical guarantees under which both and can be recovered exactly, robustly, and numerically efficiently via linear programming. Applications in array calibration and wireless communications are discussed and numerical simulations are presented, confirming and complementing our theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation"):
  with members:
    - "simultaneous sparse recovery and blind demodulation"
    - "sensor calibration for off the grid spectral estimation"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "blind deconvolution using convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-3 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-4 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "a novel millimeter wave channel simulator and applications for 5g wireless communications"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-5 (induced by source paper "simultaneous sparse recovery and blind demodulation"):
  with members:
    - "sensor calibration for off the grid spectral estimation"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "blind deconvolution using convex programming"
    - "recovering low rank matrices from few coefficients in any basis"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "support recovery for sparse signals with non stationary modulation"):
  with members:
    - "simultaneous sparse recovery and blind demodulation"
    - "sensor calibration for off the grid spectral estimation"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "blind deconvolution using convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-7 (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"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "blind calibration in compressed sensing using message passing algorithms"

Connected through hyperedge HE-8 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "hybrid approximate message passing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

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)