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: "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
  Abstract: Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering. However, popular models like Faster R-CNN rely on a costly process of annotating ground-truths for both the bounding boxes and their corresponding semantic labels, making it less amenable as a primitive task for transfer learning. In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks. The key insight is that this allows us to leverage a large amount of labeled annotations that were previously unavailable for standard object detection benchmarks. Empirically, we demonstrate that this leads to effective transfer learning and improved image captioning and visual question answering models, as measured on publicly available benchmarks.

Connected through hyperedge HE-1 (induced by source paper "connecting vision and language with localized narratives"):
  with members:
    - "dense relational captioning triple stream networks for relationship based captioning"
    - "neural sequential phrase grounding seqground"
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale interactive object segmentation with human annotators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "context and attribute grounded dense captioning"

Connected through hyperedge HE-2 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "quality estimation for image captions based on large scale human evaluations"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spice semantic propositional image caption evaluation"

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)