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 "indic handwritten script identification using offline online multimodal deep network"):
  Abstract: In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. The advantages of using character level data for training have been outlined in section I. Our method uses a multimodal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, it can work as a single framework for both offline and online script identification simultaneously which alleviates the need for designing two separate script identification modules for individual modality. One more major contribution is that we propose a novel conditional multimodal fusion scheme to combine the information from offline and online modality which takes into account the real origin of the data being fed to our network and thus it combines adaptively. An exhaustive experiment has been done on a data set consisting of English and six Indic scripts. Our proposed framework clearly outperforms different frameworks based on traditional classifiers along with handcrafted features and deep learning based methods with a clear margin. Extensive experiments show that using only character level training data can achieve state-of-art performance similar to that obtained with traditional training using word level data in our framework.

Includes members:
  Member "deep learning for word level handwritten indic script identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "handwritten indic character recognition using capsule networks")
  Member "handwriting trajectory recovery using end to end deep encoder decoder network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "script identification in natural scene image and video frames using an attention based convolutional lstm network")
  Member "cross language framework for word recognition and spotting of indic scripts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning")
  Member "hmm based writer identification in music score documents without staff line removal":
    also connected through other hyperedges:
      (none)
  Member "exploring spatial context for 3d semantic segmentation of point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors")
  Member "improving patch based scene text script identification with ensembles of conjoined networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "patch aggregator for scene text script identification")
  Member "hmm based indic handwritten word recognition using zone segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning")
  Member "local binary pattern for word spotting in handwritten historical document":
    also connected through other hyperedges:
      (none)

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)