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 "unsupervised writer adaptation for synthetic to real handwritten word recognition"):
  Abstract: Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.

Includes members:
  Member "evaluating sequence to sequence models for handwritten text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ocr4all an open source tool providing a semi automatic ocr workflow for historical printings")
  Member "handwriting recognition in low resource scripts using adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition")
  Member "hwnet v2 an efficient word image representation for handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling")
  Member "boosting handwriting text recognition in small databases with transfer learning":
    also connected through other hyperedges:
      (none)
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "zero shot deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sliced wasserstein discrepancy for unsupervised domain adaptation")
  Member "learning deep representations for word spotting under weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features")
  Member "generating synthetic data for text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images")

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)