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: "multireference alignment is easier with an aperiodic translation distribution"
  Abstract: In the multireference alignment model, a signal is observed by the action of a random circular translation and the addition of Gaussian noise. The goal is to recover the signal's orbit by accessing multiple independent observations. Of particular interest is the sample complexity, i.e., the number of observations/samples needed in terms of the signal-to-noise ratio (the signal energy divided by the noise variance) in order to drive the mean-square error (MSE) to zero. Previous work showed that if the translations are drawn from the uniform distribution, then, in the low SNR regime, the sample complexity of the problem scales as $\omega(1/\text{SNR}^3)$. In this work, using a generalization of the Chapman--Robbins bound for orbits and expansions of the $\chi^2$ divergence at low SNR, we show that in the same regime the sample complexity for any aperiodic translation distribution scales as $\omega(1/\text{SNR}^2)$. This rate is achieved by a simple spectral algorithm. We propose two additional algorithms based on non-convex optimization and expectation-maximization. We also draw a connection between the multireference alignment problem and the spiked covariance model.

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"
    - "the sample complexity of multi reference alignment"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "fundamental limits in multi image alignment"

Connected through hyperedge HE-2 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities"):
  with members:
    - "unsupervised particle sorting for high resolution single particle cryo em"
    - "a common lines approach for ab initio modeling of cyclically symmetric molecules"
    - "multi target detection with application to cryo electron microscopy"
    - "apple picker automatic particle picking a low effort cryo em framework"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "estimation in the group action channel"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"

Connected through hyperedge HE-3 (induced by source paper "toward single particle reconstruction without particle picking breaking the detection limit"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "anisotropic twicing for single particle reconstruction using autocorrelation analysis"
    - "heterogeneous multireference alignment a single pass approach"
    - "steerable principal components for space frequency localized images"
    - "fast steerable principal component analysis"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-4 (induced by source paper "estimation in the group action channel"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "sample complexity of the boolean multireference alignment problem"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"
    - "f divergence inequalities"

Connected through hyperedge HE-5 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-6 (induced by source paper "the sample complexity of multi reference alignment"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "sample complexity of the boolean multireference alignment problem"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "fundamental limits in multi image alignment"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "multireference alignment using semidefinite programming"
    - "fourier pca and robust tensor decomposition"
    - "settling the polynomial learnability of mixtures of gaussians"

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)