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: "automatic classification of defective photovoltaic module cells in electroluminescence images"
  Abstract: Abstract   Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects.  In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised segmentation of cracks on solar cells using normalized lp norm"):
  with members:
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "deep residual learning for image recognition"
    - "learned norm pooling for deep feedforward and recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast and robust detection of solar modules in electroluminescence images"):
  with members:
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "you only look once unified real time object detection"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "text line segmentation of historical documents a survey"

Connected through hyperedge HE-3 (induced by source paper "segmentation of photovoltaic module cells in electroluminescence images"):
  with members:
    - "mask r cnn"
    - "fully convolutional instance aware semantic segmentation"
    - "fast r cnn"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "camera distortion self calibration using the plumb line constraint and minimal hough entropy"

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)