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: "noise folding in compressed sensing"
  Abstract: The literature on compressed sensing has focused almost entirely on settings where the signal is noiseless and the measurements are contaminated by noise. In practice, however, the signal itself is often subject to random noise prior to measurement. We briefly study this setting and show that, for the vast majority of measurement schemes employed in compressed sensing, the two models are equivalent with the important difference that the signal-to-noise ratio (SNR) is divided by a factor proportional to p/n, where p is the dimension of the signal and n is the number of observations. Since p/n is often large, this leads to noise folding which can have a severe impact on the SNR.

Connected through hyperedge HE-1 (induced by source paper "an analysis of noise folding for low rank matrix recovery"):
  with members:
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "stable low rank matrix recovery via null space properties"
    - "robust principal component analysis"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-2 (induced by source paper "quantization in compressive sensing a signal processing approach"):
  with members:
    - "methods for quantized compressed sensing"
    - "quantization and compressive sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "linear convergence and support recovery for non convex multi penalty regularization"):
  with members:
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "analog to digital cognitive radio sampling detection and hardware"):
  with members:
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "cascade compressed carrier and doa estimation"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "sub nyquist sampling bridging theory and practice"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "wideband spectrum sensing at sub nyquist rates"

Connected through hyperedge HE-5 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"

Connected through hyperedge HE-6 (induced by source paper "non uniform wavelet sampling for rf analog to information conversion"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "stable and robust sampling strategies for compressive imaging"
    - "structured compressed sensing from theory to applications"
    - "recovery of sparsely corrupted signals"
    - "xampling signal acquisition and processing in union of subspaces"
    - "sub nyquist sampling of short pulses"

Connected through hyperedge HE-7 (induced by source paper "inexact gradient projection and fast data driven compressed sensing"):
  with members:
    - "sharp time data tradeoffs for linear inverse problems"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "matrix recipes for hard thresholding methods"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "on the snr variability in noisy compressed sensing"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "information theoretic bounds for compressed sensing"

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)