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: "rosetta large scale system for text detection and recognition in images"
  Abstract: In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta , designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta 's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.

Connected through hyperedge HE-1 (induced by source paper "deep multi modal sets"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "supervised multimodal bitransformers for classifying images and text"
    - "mfas multimodal fusion architecture search"
    - "roberta a robustly optimized bert pretraining approach"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep sets"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-3 (induced by source paper "towards vqa models that can read"):
  with members:
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "disentangling language and knowledge in task oriented dialogs"
    - "dvqa understanding data visualizations via question answering"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "iterative answer prediction with pointer augmented multimodal transformers for textvqa"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "icdar 2019 competition on scene text visual question answering"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-5 (induced by source paper "you only recognize once towards fast video text spotting"):
  with members:
    - "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"
    - "edit probability for scene text recognition"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "an anchor free region proposal network for faster r cnn based text detection approaches"
    - "towards end to end text spotting with convolutional recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "memory augmented neural networks for predictive process analytics"
    - "robust and scalable differentiable neural computer for question answering"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "implicit language model in lstm for ocr"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-7 (induced by source paper "what is wrong with scene text recognition model comparisons dataset and model analysis"):
  with members:
    - "edit probability for scene text recognition"
    - "nsml meet the mlaas platform with a real world case study"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "synthetic data for text localisation in natural images"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-8 (induced by source paper "a novel re weighting method for connectionist temporal classification"):
  with members:
    - "focal loss for dense object detection"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "reading scene text in deep convolutional sequences"
    - "synthetic data and artificial neural networks for natural scene text 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)