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: "fusion of detected objects in text for visual question answering"
  Abstract: To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The "Bounding Boxes in Text Transformer" (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark (this https URL), achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided (this https URL).

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "supervised multimodal bitransformers for classifying images and text"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"

Connected through hyperedge HE-2 (induced by source paper "xgpt cross modal generative pre training for image captioning"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attention on attention for image captioning"
    - "mass masked sequence to sequence pre training for language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-3 (induced by source paper "imagebert cross modal pre training with large scale weak supervised image text data"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "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-4 (induced by source paper "12 in 1 multi task vision and language representation learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "omninet a unified architecture for multi modal multi task learning"
    - "spanbert improving pre training by representing and predicting spans"
    - "which tasks should be learned together in multi task learning"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "multi task deep neural networks for natural language understanding"

Connected through hyperedge HE-5 (induced by source paper "vision language navigation with self supervised auxiliary reasoning tasks"):
  with members:
    - "test time training for out of distribution generalization"
    - "vision and dialog navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "scene graph generation with external knowledge and image reconstruction"
    - "discovery of useful questions as auxiliary tasks"
    - "self supervised generalisation with meta auxiliary learning"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-6 (induced by source paper "vl bert pre training of generic visual linguistic representations"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "from two graphs to n questions a vqa dataset for compositional reasoning on vision and commonsense"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-7 (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"
    - "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"
    - "deepcopy grounded response generation with hierarchical pointer networks"

Connected through hyperedge HE-8 (induced by source paper "uniter learning universal image text representations"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "selfie self supervised pretraining for image embedding"
    - "videobert a joint model for video and language representation learning"
    - "deep modular co attention networks for visual question answering"
    - "visualbert a simple and performant baseline for vision and language"

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)