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 "exponential decay of reconstruction error from binary measurements of sparse signals"):
  Abstract: Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between genetics and the presence or absence of a disease---or they may be a result of extreme quantization. In one-bit compressed sensing it has recently been shown that the number of one-bit measurements required for signal estimation mirrors that of unquantized compressed sensing. Indeed, $s$-sparse signals in $\mathbb{R}^n$ can be estimated (up to normalization) from $\Omega(s \log (n/s))$ one-bit measurements. Nevertheless, controlling the precise accuracy of the error estimate remains an open challenge. In this paper, we focus on optimizing the decay of the error as a function of the oversampling factor $\lambda := m/(s \log(n/s))$, where $m$ is the number of measurements. It is known that the error in reconstructing sparse signals from standard one-bit measurements is bounded below by $\Omega(\lambda^{-1})$. Without adjusting the measurement procedure, reducing this polynomial error decay rate is impossible. However, we show that an adaptive choice of the thresholds used for quantization may lower the error rate to $e^{-\Omega(\lambda)}$. This improves upon guarantees for other methods of adaptive thresholding as proposed in Sigma-Delta quantization. We develop a general recursive strategy to achieve this exponential decay and two specific polynomial-time algorithms which fall into this framework, one based on convex programming and one on hard thresholding. This work is inspired by the one-bit compressed sensing model, in which the engineer controls the measurement procedure. Nevertheless, the principle is extendable to signal reconstruction problems in a variety of binary statistical models as well as statistical estimation problems like logistic regression.

Includes members:
  Member "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences")
  Member "two part reconstruction in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two part reconstruction with noisy sudocodes")
  Member "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on one stage recovery for sigma delta quantized compressed sensing")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "message passing de quantization with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expectation propagation line spectral estimation")
  Member "one bit compressed sensing with non gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial 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)