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: "apple picker automatic particle picking a low effort cryo em framework"
  Abstract: Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While semi-automated particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, semi-automated particle picking is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and fully automatic particle picking. While our approach was inspired by template matching, it is completely template-free. This approach is evaluated on publicly available datasets containing micrographs of $\beta$-galactosidase and keyhole limpet hemocyanin projections.

Connected through hyperedge HE-1 (induced by source paper "image based size analysis of agglomerated and partially sintered particles via convolutional neural networks"):
  with members:
    - "fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "mask r cnn"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "no more pesky learning rate guessing games"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"

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"
    - "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"
    - "multireference alignment is easier with an aperiodic translation distribution"

Connected through hyperedge HE-3 (induced by source paper "cryo electron microscopy image analysis using multi frequency vector diffusion maps"):
  with members:
    - "the steerable graph laplacian and its application to filtering image data sets"
    - "denoising and covariance estimation of single particle cryo em images"
    - "fast steerable principal component analysis"
    - "fourier bessel rotational invariant eigenimages"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised particle sorting for high resolution single particle cryo em"):
  with members:
    - "single particle cryo electron microscopy mathematical theory computational challenges and opportunities"

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)