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: "turbo learning for captionbot and drawingbot"
  Abstract: We study in this paper the problems of both image captioning and text-to-image generation, and present a novel turbo learning approach to jointly training an image-to-text generator (a.k.a. CaptionBot) and a text-to-image generator (a.k.a. DrawingBot). The key idea behind the joint training is that image-to-text generation and text-to-image generation as dual problems can form a closed loop to provide informative feedback to each other. Based on such feedback, we introduce a new loss metric by comparing the original input with the output produced by the closed loop. In addition to the old loss metrics used in CaptionBot and DrawingBot, this extra loss metric makes the jointly trained CaptionBot and DrawingBot better than the separately trained CaptionBot and DrawingBot. Furthermore, the turbo-learning approach enables semi-supervised learning since the closed loop can provide pseudo-labels for unlabeled samples. Experimental results on the COCO dataset demonstrate that the proposed turbo learning can significantly improve the performance of both CaptionBot and DrawingBot by a large margin.

Connected through hyperedge HE-1 (induced by source paper "learning to generate grounded image captions without localization supervision"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "cycle consistency for robust visual question answering"
    - "grounding visual explanations"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "neural baby talk"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "weakly supervised dense event captioning in videos"
    - "grounded video description"

Connected through hyperedge HE-2 (induced by source paper "object driven text to image synthesis via adversarial training"):
  with members:
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stacked cross attention for image text matching"
    - "spectral normalization for generative adversarial networks"
    - "image generation from scene graphs"
    - "attention is all you need"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "shapecaptioner generative caption network for 3d shapes by learning a mapping from parts detected in multiple views to sentences"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "cross modal retrieval with implicit concept association"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "object counts bringing explicit detections back into image captioning"

Connected through hyperedge HE-4 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  with members:
    - "deep audio visual speech recognition"
    - "towards robust neural machine translation"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "end to end audiovisual fusion with lstms"

Connected through hyperedge HE-5 (induced by source paper "reinforced cross modal matching and self supervised imitation learning for vision language navigation"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "gibson env real world perception for embodied agents"
    - "self imitation learning"
    - "speaker follower models for vision and language navigation"

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)