human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "error decay of almost consistent signal estimations from quantized gaussian random projections"):
  Abstract: This paper provides new error bounds on "consistent" reconstruction methods for signals observed from quantized random projections. Those signal estimation techniques guarantee a perfect matching between the available quantized data and a new observation 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 Gaussian random 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 $\Omega(N/M)$ and former empirical validations in $O(N/M)$. Equivalently, if $M$ exceeds a minimal number of frame coefficients growing like $O(\frac{N}{\epsilon_0}\log \frac{\sqrt #R##N#N}{\epsilon_0})$, 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 $M$ Gaussian random 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 $O(\tfrac{K}{M}\log\tfrac{MN}{\sqrt K^3})$ uniformly for all such signals. Finally, we show that the proximity of vectors whose quantized random projections are only approximately consistent can still be bounded with high probability. A certain level of corruption is thus allowed in the quantization process, up to the appearance of a systematic bias in the reconstruction error of (almost) consistent signal estimates.

Includes members:
  Member "quantization and compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences")
  Member "error bounds for consistent reconstruction random polytopes and coverage processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memoryless scalar quantization for random frames")
  Member "a quantized johnson lindenstrauss lemma the finding of buffon s needle":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")
  Member "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "message passing de quantization with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expectation propagation line spectral estimation")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "frame permutation quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

Question: Please tell me which class the source paper 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)