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: "dhsegment a generic deep learning approach for document segmentation"
  Abstract: In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.

Connected through hyperedge HE-1 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers"):
  with members:
    - "using word embeddings to examine gender bias in dutch newspapers 1950 1990"
    - "the via annotation software for images audio and video"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "unsupervised text line segmentation"):
  with members:
    - "a two stage method for text line detection in historical documents"
    - "badam a public dataset for baseline detection in arabic script manuscripts"
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "unsupervised natural image patch learning"
    - "open evaluation tool for layout analysis of document images"

Connected through hyperedge HE-3 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "regular expressions for decoding of neural network outputs"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"

Connected through hyperedge HE-4 (induced by source paper "multimodal deep networks for text and image based document classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "deep contextualized word representations"
    - "mimicking word embeddings using subword rnns"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "enriching word vectors with subword information"
    - "xception deep learning with depthwise separable convolutions"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-5 (induced by source paper "multi task handwritten document layout analysis"):
  with members:
    - "learning loss functions for semi supervised learning via discriminative adversarial networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

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)