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: "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.

Connected through hyperedge HE-1 (induced by source paper "multi target detection with application to cryo electron microscopy"):
  with members:
    - "multi target detection with an arbitrary spacing distribution"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"
    - "fourier phase retrieval uniqueness and algorithms"
    - "multireference alignment is easier with an aperiodic translation distribution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "fundamental limits in multi image alignment"

Connected through hyperedge HE-2 (induced by source paper "blind identification of stochastic block models from dynamical observations"):
  with members:
    - "spectral partitioning of time varying networks with unobserved edges"
    - "blind community detection from low rank excitations of a graph filter"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "network inference from consensus dynamics"
    - "the ground truth about metadata and community detection in networks"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "reconstruction of directed networks from consensus dynamics"

Connected through hyperedge HE-3 (induced by source paper "alleviating label switching with optimal transport"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "stochastic wasserstein barycenters"
    - "parallel streaming wasserstein barycenters"
    - "multireference alignment using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "spectral methods from tensor networks"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "estimation in the group action channel"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"
    - "exact tensor completion with sum of squares"
    - "sample complexity of the boolean multireference alignment problem"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"

Connected through hyperedge HE-5 (induced by source paper "bispectrum inversion with application to multireference alignment"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "non convex phase retrieval from stft measurements"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "fundamental limits in multi image alignment"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-6 (induced by source paper "sunlayer stable denoising with generative networks"):
  with members:
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "swish a self gated activation function"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "perceptual losses for real time style transfer and super resolution"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-7 (induced by source paper "heterogeneous multireference alignment a single pass approach"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "painless breakups efficient demixing of low rank matrices"
    - "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"
    - "a max cut approach to heterogeneity in cryo electron microscopy"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "fundamental limits in multi image alignment"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

Connected through hyperedge HE-8 (induced by source paper "sample complexity of the boolean multireference alignment problem"):
  with members:
    - "fundamental limits in multi image alignment"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "multireference alignment using semidefinite 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)