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: "nocaps novel object captioning at scale"
  Abstract: Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes. Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.

Connected through hyperedge HE-1 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "robust change captioning"
    - "describing like humans on diversity in image captioning"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"
    - "semstyle learning to generate stylised image captions using unaligned text"

Connected through hyperedge HE-2 (induced by source paper "captioning images taken by people who are blind"):
  with members:
    - "attention on attention for image captioning"
    - "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"
    - "engaging image captioning via personality"
    - "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-3 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "video description a survey of methods datasets and evaluation metrics"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-4 (induced by source paper "compositional generalization in image captioning"):
  with members:
    - "the curious case of neural text degeneration"
    - "universal dependency parsing from scratch"
    - "neural baby talk"
    - "assessing composition in sentence vector representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rnns implicitly implement tensor product representations"
    - "evaluating compositionality in sentence embeddings"

Connected through hyperedge HE-5 (induced by source paper "m 2 meshed memory transformer for image captioning"):
  with members:
    - "hierarchy parsing for image captioning"
    - "image captioning transforming objects into words"
    - "augmenting self attention with persistent memory"
    - "attention on attention for image captioning"
    - "reflective decoding network for image captioning"
    - "videobert a joint model for video and language representation learning"
    - "neural baby talk"
    - "show control and tell a framework for generating controllable and grounded captions"

Connected through hyperedge HE-6 (induced by source paper "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"):
  with members:
    - "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"
    - "cross lingual language model pretraining"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modulating early visual processing by language"

Connected through hyperedge HE-7 (induced by source paper "know what you don t know modeling a pragmatic speaker that refers to objects of unknown categories"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "guided open vocabulary image captioning with constrained beam search"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "microsoft coco captions data collection and evaluation server"
    - "deep compositional captioning describing novel object categories without paired training data"

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)