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: "dense captioning with joint inference and visual context"
  Abstract: Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem. First, dense visual concept annotations in each image are associated with highly overlapping target regions, making accurate localization of each visual concept challenging. Second, the large amount of visual concepts makes it hard to recognize each of them by appearance alone. We propose a new model pipeline based on two novel ideas, joint inference and context fusion, to alleviate these two challenges. We design our model architecture in a methodical manner and thoroughly evaluate the variations in architecture. Our final model, compact and efficient, achieves state-of-the-art accuracy on Visual Genome for dense captioning with a relative gain of 73\% compared to the previous best algorithm. Qualitative experiments also reveal the semantic capabilities of our model in dense captioning.

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 "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "dense relational captioning triple stream networks for relationship based captioning"):
  with members:
    - "linknet relational embedding for scene graph"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "inferring and executing programs for visual reasoning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "context and attribute grounded dense captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "discriminability objective for training descriptive captions"
    - "modeling visual context is key to augmenting object detection datasets"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-5 (induced by source paper "geometric graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "enhanced deep learning prediction of molecular properties via augmentation of bond topology"
    - "adaptive graph convolutional neural networks"
    - "feature pyramid network for multi class land segmentation"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "neural message passing for quantum chemistry"
    - "graph convolution a high order and adaptive approach"

Connected through hyperedge HE-6 (induced by source paper "movienet a movie multilayer network model using visual and textual semantic cues"):
  with members:
    - "semantic flow in language networks"
    - "multilayer network model of movie script"
    - "centrality in modular networks"
    - "movie rating prediction using content based and link stream features"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "face detection with the faster r cnn"
    - "centrality measures for networks with community structure"

Connected through hyperedge HE-7 (induced by source paper "hindi visual genome a dataset for multimodal english to hindi machine translation"):
  with members:
    - "a visual attention grounding neural model for multimodal machine translation"
    - "imagination improves multimodal translation"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "mat a multimodal attentive translator for image captioning"
    - "multi30k multilingual english german image descriptions"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "joint event detection and description in continuous video streams"):
  with members:
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "a hierarchical approach for generating descriptive image paragraphs"

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)