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 "the sample complexity of multi reference alignment"):
  Abstract: The growing role of data-driven approaches to scientific discovery has unveiled a large class of models that involve latent transformations with a rigid algebraic constraint. Three-dimensional molecule reconstruction in Cryo-Electron Microscopy (cryo-EM) is a central problem in this class. Despite decades of algorithmic and software development, there is still little theoretical understanding of the sample complexity of this problem, that is, number of images required for 3-D reconstruction. Here we consider multi-reference alignment (MRA), a simple model that captures fundamental aspects of the statistical and algorithmic challenges arising in cryo-EM and related problems. In MRA, an unknown signal is subject to two types of corruption: a latent cyclic shift and the more traditional additive white noise. The goal is to recover the signal at a certain precision from independent samples. While at high signal-to-noise ratio (SNR), the number of observations needed to recover a generic signal is proportional to $1/\mathrm{SNR}$, we prove that it rises to a surprising $1/\mathrm{SNR}^3$ in the low SNR regime. This precise phenomenon was observed empirically more than twenty years ago for cryo-EM but has remained unexplained to date. Furthermore, our techniques can easily be extended to the heterogeneous MRA model where the samples come from a mixture of signals, as is often the case in applications such as cryo-EM, where molecules may have different conformations. This provides a first step towards a statistical theory for heterogeneous cryo-EM.

Includes members:
  Member "bispectrum inversion with application to multireference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "multireference alignment is easier with an aperiodic translation distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "sample complexity of the boolean multireference alignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral methods from tensor networks")
  Member "a representation theory perspective on simultaneous alignment and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper molecules on the representation and recovery of dynamical structures with application to flexible macro molecular structures in cryo em")
  Member "fundamental limits in multi image alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite sample analysis of image registration")
  Member "non unique games over compact groups and orientation estimation in cryo em":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "multireference alignment using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations")
  Member "fourier pca and robust tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning two layer rectified neural networks in polynomial time")

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)