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: "cross language framework for word recognition and spotting of indic scripts"
  Abstract: Abstract   Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script). Training with one source script and testing with another script to have a reasonable result is not easy in handwriting domain due to the complex nature of handwriting variability among scripts. Also it is difficult in mapping between source and target characters when they appear in cursive word images. The proposed Indic cross language framework exploits a large resource of dataset for training and uses it for recognizing and spotting text of other target scripts where sufficient amount of training data is not available. Since, Indic scripts are mostly written in 3 zones, namely, upper, middle and lower, we employ zone-wise character (or component) mapping for efficient learning purpose. The performance of our cross-language framework depends on the extent of similarity between the source and target scripts. Hence, we devise an entropy based script similarity score using source to target character mapping that will provide a feasibility of cross language transcription. We have tested our approach in three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the corresponding results are reported.

Connected through hyperedge HE-1 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "confidence prediction for lexicon free ocr"
    - "vital visual tracking via adversarial learning"
    - "synthetic data generation for indic handwritten text recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "adversarial discriminative domain adaptation"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "indic handwritten script identification using offline online multimodal deep network"):
  with members:
    - "deep learning for word level handwritten indic script identification"
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "hmm based writer identification in music score documents without staff line removal"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "hmm based indic handwritten word recognition using zone segmentation"
    - "local binary pattern for word spotting in handwritten historical document"
    - "compact bilinear pooling"

Connected through hyperedge HE-3 (induced by source paper "word searching in scene image and video frame in multi script scenario using dynamic shape coding"):
  with members:
    - "date field retrieval in scene image and video frames using text enhancement and shape coding"
    - "multi oriented text detection and verification in video frames and scene images"
    - "text recognition in scene image and video frame using color channel selection"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "hmm based indic handwritten word recognition using zone segmentation"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "reading scene text in deep convolutional sequences"
    - "reading text in the wild with convolutional neural networks"

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)