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: "covariance estimation using conjugate gradient for 3d classification in cryo em"
  Abstract: Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.

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"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "non unique games over compact groups and orientation estimation 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"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "fundamental limits in multi image alignment"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"

Connected through hyperedge HE-3 (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"
    - "a representation theory perspective on simultaneous alignment and classification"

Connected through hyperedge HE-4 (induced by source paper "a representation theory perspective on simultaneous alignment and classification"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "heterogeneous multireference alignment a single pass approach"
    - "multisection in the stochastic block model using semidefinite programming"
    - "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"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "multireference alignment using semidefinite programming"

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"
    - "a representation theory perspective on simultaneous alignment and classification"
    - "non unique games over compact groups and orientation estimation in cryo em"

Connected through hyperedge HE-6 (induced by source paper "denoising and covariance estimation of single particle cryo em images"):
  with members:
    - "orthogonal matrix retrieval in cryo electron microscopy"
    - "fast steerable principal component analysis"

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)