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 tell and discriminate image captioning by self retrieval with partially labeled data"
  Abstract: The aim of image captioning is to generate similar captions by machine as human do to describe image contents. Despite many efforts, generating discriminative captions for images remains non-trivial. Most traditional approaches imitate the language structure patterns, thus tend to fall into a stereotype of replicating frequent phrases or sentences and neglect unique aspects of each image. In this work, we propose an image captioning framework with a self-retrieval module as training guidance, which encourages generating discriminative captions. It brings unique advantages: (1) the self-retrieval guidance can act as a metric and an evaluator of caption discriminativeness to assure the quality of generated captions. (2) The correspondence between generated captions and images are naturally incorporated in the generation process without human annotations, and hence our approach could utilize a large amount of unlabeled images to boost captioning performance with no additional laborious annotations. We demonstrate the effectiveness of the proposed retrieval-guided method on MS-COCO and Flickr30k captioning datasets, and show its superior captioning performance with more discriminative captions.

Connected through hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "multimodal image captioning for marketing analysis"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"

Connected through hyperedge HE-2 (induced by source paper "better captioning with sequence level exploration"):
  with members:
    - "exploring visual relationship for image captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "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"
    - "video captioning with guidance of multimodal latent topics"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-3 (induced by source paper "improving referring expression grounding with cross modal attention guided erasing"):
  with members:
    - "bilinear attention networks"
    - "dropblock a regularization method for convolutional networks"
    - "stacked cross attention for image text matching"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "mattnet modular attention network for referring expression comprehension"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual reasoning with multi hop feature modulation"

Connected through hyperedge HE-4 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "sparse sequence to sequence models"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "sparse and constrained attention for neural machine translation"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "tell the difference fine grained visual descriptor via a discriminating referee"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "detecting visual relationships with deep relational networks"
    - "dense captioning events in videos"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "reasoning about fine grained attribute phrases using reference games"
    - "dynamic computational time for visual attention"

Connected through hyperedge HE-6 (induced by source paper "image captioning with very scarce supervised data adversarial semi supervised learning approach"):
  with members:
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "unsupervised image captioning"
    - "textual explanations for self driving vehicles"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "iterative learning with open set noisy labels"
    - "joint training for neural machine translation models with monolingual data"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-7 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "using artificial tokens to control languages for multilingual image caption generation"

Connected through hyperedge HE-8 (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"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship 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)