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: "segmentation of photovoltaic module cells in electroluminescence images"
  Abstract: High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kind of defects, which is time-consuming and expensive. In this work, we make an important step towards improving the current state-of-the-art in solar module inspection. We propose a robust automated segmentation method to extract individual solar cells from EL images of PV modules. Automated segmentation of cells is a key step in automating the visual inspection workflow. It also enables controlled studies on large amounts of data to understanding the effects of module degradation over time - a process not yet fully understood. The proposed method infers in several steps a high level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 96.09% and an $F_1$ score of 97.23%.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised segmentation of cracks on solar cells using normalized lp norm"):
  with members:
    - "automatic classification of defective 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 "automatic classification of defective photovoltaic module cells in electroluminescence images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "exploiting local features from deep networks for image retrieval"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "fast and robust detection of solar modules in electroluminescence images"):
  with members:
    - "automatic classification of defective 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"

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)