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 "bispectrum inversion with application to multireference alignment"):
  Abstract: We consider the problem of estimating a signal from noisy circularly translated versions of itself, called   multireference alignment  (MRA). One natural approach to MRA could be to estimate the shifts of the observations first, and infer the signal by aligning and averaging the data. In contrast, we consider a method based on estimating the signal directly, using features of the signal that are invariant under translations. Specifically, we estimate the power spectrum and the bispectrum of the signal from the observations. Under mild assumptions, these invariant features contain enough information to infer the signal. In particular, the bispectrum can be used to estimate the Fourier phases. To this end, we propose and analyze a few algorithms. Our main methods consist of nonconvex optimization over the smooth manifold of phases. Empirically, in the absence of noise, these nonconvex algorithms appear to converge to the target signal with random initialization. The algorithms are also robust to noise. We then suggest three additional methods. These methods are based on frequency marching, semidefinite relaxation, and integer programming. The first two methods provably recover the phases exactly in the absence of noise. In the high noise level regime, the invariant features approach for MRA results in stable estimation if the number of measurements scales like the cube of the noise variance, which is the information-theoretic rate. Additionally, it requires only one pass over the data, which is important at low signal-to-noise ratio when the number of observations must be large.

Includes members:
  Member "message passing algorithms for synchronization problems over compact groups":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi frequency phase synchronization")
  Member "on signal reconstruction from frog measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "fourier phase retrieval uniqueness and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "on the uniqueness of frog methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "the sample complexity of multi reference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "non convex phase retrieval from stft measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "the projected power method an efficient algorithm for joint alignment from pairwise differences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal learning of joint alignments with a faulty oracle")
  Member "fundamental limits in multi image alignment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finite sample analysis of image registration")

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)