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 "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"):
  Abstract: This paper introduces a new algorithm for the so-called “analysis problem” in quantization of finite frame representations that provides a near-optimal solution in the case of random measurements. The main contributions include the development of a general quantization framework called distributed noise shaping and, in particular, beta duals of frames, as well as the performance analysis of beta duals in both deterministic and probabilistic settings. It is shown that for random frames, using beta duals results in near-optimally accurate reconstructions with respect to both the frame redundancy and the number of levels at which the frame coefficients are quantized. More specifically, for any frame E of m vectors in \(\mathbb {R}^k\) except possibly from a subset of Gaussian measure exponentially small in m and for any number \(L \ge 2\) of quantization levels per measurement to be used to encode the unit ball in \(\mathbb {R}^k\), there is an algorithmic quantization scheme and a dual frame together that guarantee a reconstruction error of at most \(\sqrt{k}L^{-(1-\eta )m/k}\), where \(\eta \) can be arbitrarily small for sufficiently large problems. Additional features of the proposed algorithm include low computational cost and parallel implementability.

Includes members:
  Member "exponential decay of reconstruction error from binary measurements of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "on one stage recovery for sigma delta quantized compressed sensing")
  Member "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)