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 "taking the edge off quantization projected back projection in dithered compressive sensing"):
  Abstract: Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any practical sensor design. To characterize the signal reconstruction quality in this framework, most of the existing theoretical analyses lie heavily on the quantization of sub-Gaussian random projections (e.g., Gaussian or Bernoulli). We show here that a simple uniform scalar quantizer is compatible with a large class of random sensing matrices known to respect, with high probability, the restricted isometry property (RIP). Critically, this compatibility arises from the addition of a uniform random vector, or "dithering", to the linear signal observations before quantization. In this setting, we prove the existence of (at least) one signal reconstruction method, i.e., the projected back projection (PBP), whose reconstruction error decays when the number of quantized measurements increases. This holds with high probability in the estimation of sparse signals and low-rank matrices. We validate numerically the predicted error decay as the number of measurements increases.

Includes members:
  Member "quantized compressive sensing with rip matrices the benefit of dithering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "fast binary embeddings and quantized compressed sensing with structured matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds")
  Member "one bit compressed sensing with partial gaussian circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "optimizing quantization for lasso recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "time for dithering fast and quantized random embeddings via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "isometric sketching of any set via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "small width low distortions quantized random embeddings of low complexity sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "the generalized lasso with non linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")

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)