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 max cut approach to heterogeneity in cryo electron microscopy"
  Abstract: The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to improvements in the hardware of the microscopes. Yet, one of the key open challenges of the field remains the processing of heterogeneous data sets, produced from samples containing particles at several different conformational states. For such data sets, one must first classify their images into homogeneous groups, where each group corresponds to the same underlying structure, followed by reconstruction of a three-dimensional model from each of the homogeneous groups. This task has been proven to be extremely difficult. In this paper we present an iterative algorithm for processing heterogeneous data sets that combines the classification and reconstruction steps. We prove accuracy and stability bounds on the algorithm, and demonstrate it on simulated as well as experimental data sets.

Connected through hyperedge HE-1 (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 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-2 (induced by source paper "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions"):
  with members:
    - "a representation theory perspective on simultaneous alignment and classification"
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"

Connected through hyperedge HE-3 (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"
    - "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"
    - "covariance estimation using conjugate gradient for 3d classification 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.CV(Computer Vision and Pattern Recognition)