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: "a representation theory perspective on simultaneous alignment and classification"
  Abstract: One of the difficulties in 3D reconstruction of molecules from images in single particle Cryo-Electron Microscopy (Cryo-EM), in addition to high levels of noise and unknown image orientations, is heterogeneity in samples: in many cases, the samples contain a mixture of molecules, or multiple conformations of one molecule. Many algorithms for the reconstruction of molecules from images in heterogeneous Cryo-EM experiments are based on iterative approximations of the molecules in a non-convex optimization that is prone to reaching suboptimal local minima. Other algorithms require an alignment in order to perform classification, or vice versa. The recently introduced Non-Unique Games framework provides a representation theoretic approach to studying problems of alignment over compact groups, and offers convex relaxations for alignment problems which are formulated as semidefinite programs (SDPs) with certificates of global optimality under certain circumstances. In this manuscript, we propose to extend Non-Unique Games to the problem of simultaneous alignment and classification with the goal of simultaneously classifying Cryo-EM images and aligning them within their respective classes. Our proposed approach can also be extended to the case of continuous heterogeneity.

Connected through hyperedge HE-1 (induced by source paper "hyper molecules on the representation and recovery of dynamical structures with application to flexible macro molecular structures in cryo em"):
  with members:
    - "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"):
  with members:
    - "a max cut approach to heterogeneity in cryo electron microscopy"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

Connected through hyperedge HE-5 (induced by source paper "a max cut approach to heterogeneity in cryo electron microscopy"):
  with members:
    - "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

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)