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: "deepdiva a highly functional python framework for reproducible experiments"
  Abstract: We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment with a very limited amount of information or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):
  with members:
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "do better imagenet models transfer better"
    - "offline signature verification by combining graph edit distance and triplet networks"
    - "deep learning for classical japanese literature"
    - "identifying cross depicted historical motifs"
    - "open evaluation tool for layout analysis of document images"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"

Connected through hyperedge HE-2 (induced by source paper "trainable spectrally initializable matrix transformations in convolutional neural networks"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "squeeze and excitation networks"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep scattering spectrum"

Connected through hyperedge HE-3 (induced by source paper "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"):
  with members:
    - "icdar 2019 historical document reading challenge on large structured chinese family records"
    - "open evaluation tool for layout analysis of document images"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "are you tampering with my data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "towards an understanding of neural networks in natural image spaces"

Connected through hyperedge HE-5 (induced by source paper "identifying cross depicted historical motifs"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "squeeze and excitation networks"
    - "deep residual learning for image recognition"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"
    - "learning to compare image patches via convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "offline signature verification by combining graph edit distance and triplet networks"):
  with members:
    - "offline handwritten signature verification literature review"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"
    - "deep metric learning using triplet network"

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)