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 "quantized compressed sensing by rectified linear units"):
  Abstract: This work is concerned with the problem of recovering high-dimensional signals $\mathbf{x} \in \mathbb{R}^n$ which belong to a convex set of low-complexity from a small number of quantized measurements. We propose to estimate the signals via a convex program based on rectified linear units (ReLUs) for two different quantization schemes, namely one-bit and uniform multi-bit quantization. Assuming that the linear measurement process can be modelled by a sensing matrix with i.i.d. subgaussian rows, we obtain for both schemes near-optimal uniform reconstruction guarantees by adding well-designed noise to the linear measurements prior to the quantization step. In the one-bit case, we show that the program is robust against adversarial bit corruptions as well as additive noise on the linear measurements. Further, our analysis quantifies precisely how the rate-distortion relationship of the program changes depending on whether we seek reconstruction accuracies above or below the noise floor. The proofs rely on recent results by Dirksen and Mendelson on non-Gaussian hyperplane tessellations. Finally, we complement our theoretical analysis with numerical experiments which compare our method to other state-of-the-art methodologies.

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 "non gaussian hyperplane tessellations and robust one bit compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "robust one bit compressed sensing with partial circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "on recovery guarantees for one bit compressed sensing on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "methods for quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "superset technique for approximate recovery in one bit compressed sensing")
  Member "the generalized lasso with non linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "error decay of almost consistent signal estimations from quantized gaussian random projections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "one bit sensing of low rank and bisparse matrices")

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)