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: "engaging image captioning via personality"
  Abstract: Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., "a man playing a guitar"). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations (Mazare et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

Connected through hyperedge HE-1 (induced by source paper "captioning images taken by people who are blind"):
  with members:
    - "attention on attention for image captioning"
    - "nocaps novel object captioning at scale"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "a comprehensive survey of deep learning for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "stair captions constructing a large scale japanese image caption dataset"

Connected through hyperedge HE-2 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "moving beyond the turing test with the allen ai science challenge"
    - "vqa visual question answering"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-3 (induced by source paper "towards unsupervised image captioning with shared multimodal embeddings"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "unsupervised image captioning"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-4 (induced by source paper "the dialogue dodecathlon open domain knowledge and image grounded conversational agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "the curious case of neural text degeneration"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-5 (induced by source paper "evaluating text to image matching using binary image selection bison"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "stacked cross attention for image text matching"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to ask neural question generation for reading comprehension"
    - "context aware captions from context agnostic supervision"
    - "convolutional image captioning"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-6 (induced by source paper "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-7 (induced by source paper "all in one image grounded conversational agents"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "towards vqa models that can read"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "real or fake learning to discriminate machine from human generated text"
    - "12 in 1 multi task vision and language representation learning"

Connected through hyperedge HE-8 (induced by source paper "hal improved text image matching by mitigating visual semantic hubs"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "a strong and robust baseline for text image matching"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "improving generalization via scalable neighborhood component analysis"
    - "sampling matters in deep embedding learning"
    - "foil it find one mismatch between image and language caption"
    - "learning two branch neural networks for image text matching tasks"

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)