human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a two stage method for text line detection in historical documents"):
  Abstract: This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator and other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images (#N#                  #N#                    #N#                  #N#                  $$<\,50$$#N#                  #N#                    #N#                  #N#                ). This is achieved by utilizing data augmentation strategies. The network predictions are used as input for the second stage which performs a bottom-up clustering to build baselines. The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines. It substantially outperforms current state-of-the-art approaches. For example, for the complex track of the cBAD: ICDAR2017 Competition on Baseline Detection the F value is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.

Includes members:
  Member "baseline detection in historical documents using convolutional u nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "badam a public dataset for baseline detection in arabic script manuscripts")
  Member "dhsegment a generic deep learning approach for document segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "multi task handwritten document layout analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "badam a public dataset for baseline detection in arabic script manuscripts")
  Member "pagenet page boundary extraction in historical handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sliding line point regression for shape robust scene text detection")
  Member "read bad a new dataset and evaluation scheme for baseline detection in archival documents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a computationally efficient pipeline approach to full page offline handwritten text recognition")
  Member "full page text recognition learning where to start and when to stop":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "field typing for improved recognition on heterogeneous handwritten forms")
  Member "phocnet a deep convolutional neural network for word spotting in handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling")
  Member "regular expressions for decoding of neural network outputs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalize symbolic knowledge with neural rule engine")

Question: Please tell me which class the source paper 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)