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 and compressive sensing"):
  Abstract: Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta (Σ Δ) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms, and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.

Includes members:
  Member "error decay of almost consistent signal estimations from quantized random gaussian projections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property")
  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 "error decay of almost consistent signal estimations from quantized gaussian random projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "an rip based approach to sigma delta quantization for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "adapted decimation on finite frames for arbitrary orders of sigma delta quantization")
  Member "robust 1 bit compressive sensing via gradient support pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonconvex penalties with analytical solutions for one bit compressive sensing")
  Member "a quantized johnson lindenstrauss lemma the finding of buffon s needle":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")

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)