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 "serial quantization for sparse time sequences"):
  Abstract: Sparse signals are encountered in a broad range of applications. In order to process these signals using digital hardware, they must be first sampled and quantized using an analog-to-digital convertor (ADC), which typically operates in a serial scalar manner. In this work, we propose a method for serial quantization of sparse time sequences (SQuaTS) inspired by group testing theory, which is designed to reliably and accurately quantize sparse signals acquired in a sequential manner using serial scalar ADCs. Unlike previously proposed approaches which combine quantization and compressed sensing (CS), our SQuaTS scheme updates its representation on each incoming analog sample and does not require the complete signal to be observed and stored in analog prior to quantization. We characterize the asymptotic tradeoff between accuracy and quantization rate of SQuaTS as well as its computational burden. We also propose a variation of SQuaTS, which trades rate for computational efficiency. Next, we show how SQuaTS can be naturally extended to distributed quantization scenarios, where a set of jointly sparse time sequences are acquired individually and processed jointly. Our numerical results demonstrate that SQuaTS is capable of achieving substantially improved representation accuracy over previous CS-based schemes without requiring the complete set of analog signal samples to be observed prior to its quantization, making it an attractive approach for acquiring sparse time sequences.

Includes members:
  Member "learning distributions from their samples under communication constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "information theoretic and algorithmic thresholds for group testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "efficient data collection over multiple access wireless sensors network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serial quantization for representing sparse signals")
  Member "efficient nonrandom construction and decoding of non adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing")
  Member "quantization of compressive samples with stable and robust recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for representing sparse signals")
  Member "quantization and compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for representing sparse signals")
  Member "distributed quantization for measurement of correlated sparse sources over noisy channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed quantization for sparse time sequences")
  Member "distributed compressed sensing for static and time varying networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed 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)