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: "error decay of almost consistent signal estimations from quantized random gaussian projections"
  Abstract: This paper provides new error bounds on "consistent" reconstruction methods for signals observed from quantized random sensing. Those signal estimation techniques guarantee a perfect matching between the available quantized data and a reobservation of the estimated signal under the same sensing model. Focusing on dithered uniform scalar quantization of resolution $\delta>0$, we prove first that, given a random Gaussian frame of $\mathbb R^N$ with $M$ vectors, the worst case $\ell_2$-error of consistent signal reconstruction decays with high probability as $O(\frac{N}{M}\log\frac{M}{\sqrt N})$ uniformly for all signals of the unit ball $\mathbb B^N \subset \mathbb R^N$. Up to a log factor, this matches a known lower bound in $\Omega(N/M)$. Equivalently, with a minimal number of frame coefficients behaving like $M = O(\frac{N}{\epsilon_0}\log(\frac{\sqrt #R##N#N}{\epsilon_0}))$, any vectors in $\mathbb B^N$ with $M$ identical quantized projections are at most $\epsilon_0$ apart with high probability. Second, in the context of Quantized Compressed Sensing with $M$ random Gaussian measurements and under the same scalar quantization scheme, consistent reconstructions of $K$-sparse signals of $\mathbb R^N$ have a worst case error that decreases with high probability as $O(\frac{K}{M}\log\frac{MN}{\sqrt K^3})$ uniformly for all such signals. Finally, we show that the strict consistency condition can be slightly relaxed, e.g., allowing for a bounded level of error in the quantization process, while still guaranteeing a proximity between the original and the estimated signal. In particular, if this quantization error is of order $O(1)$ with respect to $M$, similar worst case error decays are reached for reconstruction methods adjusted to such an approximate consistency.

Connected through hyperedge HE-1 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

Connected through hyperedge HE-2 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the gap between rip properties and sparse recovery conditions"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "quantization and compressive sensing"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"

Connected through hyperedge HE-3 (induced by source paper "quantization and compressive sensing"):
  with members:
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "small width low distortions quantized random embeddings of low complexity sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

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)