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: "towards vqa models that can read"
  Abstract: Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new "TextVQA" dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0.

Connected through hyperedge HE-1 (induced by source paper "in defense of grid features for visual question answering"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "visualbert a simple and performant baseline for vision and language"
    - "unified vision language pre training for image captioning and vqa"
    - "answer them all toward universal visual question answering models"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-2 (induced by source paper "visual question answering on 360 deg images"):
  with members:
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-4 (induced by source paper "plotqa reasoning over scientific plots"):
  with members:
    - "bilinear attention networks"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-5 (induced by source paper "imagebert cross modal pre training with large scale weak supervised image text data"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "fusion of detected objects in text for 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"
    - "visualbert a simple and performant baseline for vision and language"
    - "unified vision language pre training for image captioning and vqa"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "position focused attention network for image text matching"

Connected through hyperedge HE-6 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "deep modular co attention networks for visual question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-7 (induced by source paper "on the general value of evidence and bilingual scene text visual question answering"):
  with members:
    - "omnidirectional scene text detection with sequential free box discretization"
    - "chinese street view text large scale chinese text reading with partially supervised learning"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "visual question answering datasets algorithms and future challenges"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"

Connected through hyperedge HE-8 (induced by source paper "scene text visual question answering"):
  with members:
    - "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"
    - "rosetta large scale system for text detection and recognition in images"

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.CL(Computation and Language)