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Center node: "historical document image segmentation with lda initialized deep neural networks"
  Abstract: In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA). Typically, the weights of a deep neural network are initialized with: random values, greedy layer-wise pre-training (usually as Deep Belief Network or as auto-encoder) or by re-using the layers from another network (transfer learning). Hence, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn an LDA into either a neural layer or a classification layer. We analyze the initialization technique on historical documents. First, we show that an LDA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis at pixel level, we investigate the effectiveness of LDA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):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"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "identifying cross depicted historical motifs"
    - "open evaluation tool for layout analysis of document images"
    - "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"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "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"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "open evaluation tool for layout analysis of document images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deepdiva a highly functional python framework for reproducible experiments"):
  with members:
    - "identifying cross depicted historical motifs"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep residual learning for image recognition"
    - "openml networked science in machine learning"
    - "learning fine grained image similarity with deep ranking"

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)