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: "fundamental limits in multi image alignment"
  Abstract: The performance of multiimage alignment, bringing different images into one coordinate system, is critical in many applications with varied signal-to-noise ratio (SNR) conditions. A great amount of effort is being invested into developing methods to solve this problem. Several important questions thus arise, including: Which are the fundamental limits in multiimage alignment performance? Does having access to more images improve the alignment? Theoretical bounds provide a fundamental benchmark to compare methods and can help establish whether improvements can be made. In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available. We derive and analyze the Cramer-Rao and Ziv-Zakai lower bounds under different statistical models for the underlying image. We show the existence of different behavior zones depending on the difficulty level of the problem, given by the SNR conditions of the input images. The analysis we present here brings further insight into the fundamental limitations of the multiimage alignment problem.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of image registration"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a deep metric for multimodal registration"
    - "demystifying fixed k nearest neighbor information estimators"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"

Connected through hyperedge HE-2 (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"
    - "multireference alignment is easier with an aperiodic translation distribution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multi target detection with an arbitrary spacing distribution"):
  with members:
    - "multi target detection with application to cryo electron microscopy"
    - "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"
    - "manopt a matlab toolbox for optimization on manifolds"

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"
    - "the sample complexity of multi reference alignment"
    - "non convex phase retrieval from stft measurements"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-6 (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"
    - "the sample complexity of multi reference alignment"
    - "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"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

Connected through hyperedge HE-7 (induced by source paper "the sample complexity of multi reference alignment"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "multireference alignment is easier with an aperiodic translation distribution"
    - "sample complexity of the boolean multireference alignment problem"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "universal joint image clustering and registration using partition information"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "sample complexity of the boolean multireference alignment problem"
    - "info clustering a mathematical theory for data clustering"
    - "universal clustering via crowdsourcing"

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.CV(Computer Vision and Pattern Recognition)