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: "show control and tell a framework for generating controllable and grounded captions"
  Abstract: Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: this https URL.

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"
    - "nocaps novel object captioning at scale"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"

Connected through hyperedge HE-2 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "annotating object instances with a polygon rnn"
    - "twin networks matching the future for sequence generation"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "self critical sequence training for image captioning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (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-4 (induced by source paper "select and attend towards controllable content selection in text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "towards content transfer through grounded text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"

Connected through hyperedge HE-6 (induced by source paper "smart training shallow memory aware transformers for robotic explainability"):
  with members:
    - "embodied vision and language navigation with dynamic convolutional filters"
    - "image captioning transforming objects into words"
    - "augmenting self attention with persistent memory"
    - "neural baby talk"
    - "probabilistic object detection definition and evaluation"
    - "on evaluation of embodied navigation agents"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "contcap a comprehensive framework for continual image captioning"):
  with members:
    - "fine grained continual learning"
    - "incremental learning techniques for semantic segmentation"
    - "incremental classifier learning with generative adversarial networks"
    - "neural baby talk"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "continuous learning in single incremental task scenarios"
    - "end to end incremental learning"
    - "progressive neural networks"

Connected through hyperedge HE-8 (induced by source paper "towards diverse and accurate image captions via reinforcing determinantal point process"):
  with members:
    - "describing like humans on diversity in image captioning"
    - "discriminability objective for training descriptive captions"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

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)