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: "sequence to label script identification for multilingual ocr"
  Abstract: We describe a novel line-level script identification method. Previous work repurposed an OCR model generating per-character script codes, counted to obtain line-level script identification. This has two shortcomings. First, as a sequence-to-sequence model it is more complex than necessary for the sequence-to-label problem of line script identification. This makes it harder to train and inefficient to run. Second, the counting heuristic may be suboptimal compared to a learned model. Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer. The encoder converts a line image into a feature sequence. The summarizer aggregates the sequence to classify the line. We test various summarizers with identical inception-style convolutional networks as encoders. Experiments on scanned books and photos containing 232 languages in 30 scripts show 16% reduction of script identification error rate compared to the baseline. This improved script identification reduces the character error rate attributable to script misidentification by 33%.

Connected through hyperedge HE-1 (induced by source paper "towards unconstrained end to end text spotting"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "msr multi scale shape regression for scene text detection"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-2 (induced by source paper "patch aggregator for scene text script identification"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "fast multi language lstm based online handwriting recognition"):
  with members:
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "fots fast oriented text spotting with a unified network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"
    - "deep scene text detection with connected component proposals"
    - "deep direct regression for multi oriented scene text detection"

Connected through hyperedge HE-5 (induced by source paper "recognizing challenging handwritten annotations with fully convolutional networks"):
  with members:
    - "real time document image classification using deep cnn and extreme learning machines"
    - "document image binarization with fully convolutional neural networks"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)