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 quantization for compressed sensing"):
  Abstract: We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.

Includes members:
  Member "performance bounds for vector quantized compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis by synthesis based quantization of compressed sensing measurements")
  Member "distributed functional scalar quantization simplified":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on distributed quantization for classification")
  Member "analysis by synthesis quantization for compressed sensing measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing")
  Member "message passing de quantization with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "expectation propagation line spectral estimation")
  Member "regime change bit depth versus measurement rate in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "optimal quantization for compressive sensing under message passing reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "compressive sensing over networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "universal rate efficient scalar quantization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalar quantization as sparse least square optimization")

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)