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 "fast and robust compressive phase retrieval with sparse graph codes"):
  Abstract: In this paper, we tackle the compressive phase retrieval problem in the presence of noise. The noisy compressive phase retrieval problem is to recover a $K$-sparse complex signal $s \in \mathbb{C}^n$, from a set of $m$ noisy quadratic measurements: $ y_i=| a_i^H s |^2+w_i$, where $a_i^H\in\mathbb{C}^n$ is the $i$th row of the measurement matrix $A\in\mathbb{C}^{m\times n}$, and $w_i$ is the additive noise to the $i$th measurement. We consider the regime where $K=\beta n^\delta$, with constants $\beta>0$ and $\delta\in(0,1)$. We use the architecture of PhaseCode algorithm, and robustify it using two schemes: the almost-linear scheme and the sublinear scheme. We prove that with high probability, the almost-linear scheme recovers $s$ with sample complexity $\Theta(K \log(n))$ and computational complexity $\Theta(n \log(n))$, and the sublinear scheme recovers $s$ with sample complexity $\Theta(K\log^3(n))$ and computational complexity $\Theta(K\log^3(n))$. To the best of our knowledge, this is the first scheme that achieves sublinear computational complexity for compressive phase retrieval problem. Finally, we provide simulation results that support our theoretical contributions.

Includes members:
  Member "phase retrieval via wirtinger flow theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "phasecode fast and efficient compressive phase retrieval based on sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "sub linear time support recovery for compressed sensing using sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse graph codes for non adaptive quantitative group testing")
  Member "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an adaptive sublinear time block sparse fourier transform")
  Member "sparse phase retrieval convex algorithms and limitations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the capacity of waveform channels under square law detection of time limited signals")
  Member "sparse signal recovery from quadratic measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval with polarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hardness results for structured linear systems")
  Member "phaselift exact and stable signal recovery from magnitude measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")

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)