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 "quantization of compressive samples with stable and robust recovery"):
  Abstract: In this paper we study the quantization stage that is implicit in any compressed sensing signal acquisition paradigm. We propose using Sigma-Delta quantization and a subsequent reconstruction scheme based on convex optimization. We prove that the reconstruction error due to quantization decays polynomially in the number of measurements. Our results apply to arbitrary signals, including compressible ones, and account for measurement noise. Additionally, they hold for sub-Gaussian (including Gaussian and Bernoulli) random compressed sensing measurements, as well as for both high bit-depth and coarse quantizers, and they extend to 1-bit quantization. In the noise-free case, when the signal is strictly sparse we prove that by optimizing the order of the quantization scheme one can obtain root-exponential decay in the reconstruction error due to quantization.

Includes members:
  Member "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds")
  Member "quantization and compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serial quantization for sparse time sequences")
  Member "noise shaping quantization methods for frame based and compressive sampling systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapted decimation on finite frames for arbitrary orders of sigma delta quantization")
  Member "recovery analysis for weighted ell_1 minimization using a null space property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information")
  Member "an rip based approach to sigma delta quantization for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices")
  Member "exponential decay of reconstruction error from binary measurements of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "near optimal encoding for sigma delta quantization of finite frame expansions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adapted decimation on finite frames for arbitrary orders of sigma delta quantization")
  Member "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on one stage recovery for sigma delta quantized compressed sensing")

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)