human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on fast decoding of high dimensional signals from one bit measurements"
  Abstract: In the problem of one-bit compressed sensing, the goal is to find a $\delta$-close estimation of a $k$-sparse vector $x \in \mathbb{R}^n$ given the signs of the entries of $y = \Phi x$, where $\Phi$ is called the measurement matrix. For the one-bit compressed sensing problem, previous work \cite{Plan-robust,support} achieved $\Theta (\delta^{-2} k \log(n/k))$ and $\tilde{ \Oh} ( \frac{1}{ \delta } k \log (n/k))$ measurements, respectively, but the decoding time was $\Omega ( n k \log (n / k ))$. \ In this paper, using tools and techniques developed in the context of two-stage group testing and streaming algorithms, we contribute towards the direction of very fast decoding time. We give a variety of schemes for the different versions of one-bit compressed sensing, such as the for-each and for-all version, support recovery; all these have $poly(k, \log n)$ decoding time, which is an exponential improvement over previous work, in terms of the dependence of $n$.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "one bit expandersketch for one bit compressed sensing"):
  with members:
    - "improved bounds for universal one bit compressive sensing"
    - "one bit compressive sensing of dictionary sparse signals"
    - "heavy hitters via cluster preserving clustering"
    - "for all sparse recovery in near optimal time"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"

Question: Please tell me which class the center node 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)