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: "from images to sentences through scene description graphs using commonsense reasoning and knowledge"
  Abstract: In this paper we propose the construction of linguistic descriptions of images. This is achieved through the extraction of scene description graphs (SDGs) from visual scenes using an automatically constructed knowledge base. SDGs are constructed using both vision and reasoning. Specifically, commonsense reasoning is applied on (a) detections obtained from existing perception methods on given images, (b) a "commonsense" knowledge base constructed using natural language processing of image annotations and (c) lexical ontological knowledge from resources such as WordNet. Amazon Mechanical Turk(AMT)-based evaluations on Flickr8k, Flickr30k and MS-COCO datasets show that in most cases, sentences auto-constructed from SDGs obtained by our method give a more relevant and thorough description of an image than a recent state-of-the-art image caption based approach. Our Image-Sentence Alignment Evaluation results are also comparable to that of the recent state-of-the art approaches.

Connected through hyperedge HE-1 (induced by source paper "relational reasoning using prior knowledge for visual captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "improving image captioning by leveraging knowledge graphs"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning visual knowledge memory networks for visual question answering"
    - "object hallucination in image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-2 (induced by source paper "tiger text to image grounding for image caption evaluation"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "stacked cross attention for image text matching"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "neural approaches to conversational ai"
    - "learning to evaluate image captioning"
    - "learning two branch neural networks for image text matching tasks"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-3 (induced by source paper "reo relevance extraness omission a fine grained evaluation for image captioning"):
  with members:
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "stacked cross attention for image text matching"
    - "neural approaches to conversational ai"
    - "object hallucination in image captioning"
    - "learning to evaluate image captioning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-4 (induced by source paper "object relation detection based on one shot learning"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting visual relationships with deep relational networks"
    - "squeeze and excitation networks"
    - "scene graph generation from objects phrases and region captions"
    - "visual translation embedding network for visual relation detection"
    - "learning feed forward one shot learners"
    - "spice semantic propositional image caption evaluation"
    - "automatic description generation from images a survey of models datasets and evaluation measures"

Connected through hyperedge HE-5 (induced by source paper "detecting visual relationships with deep relational networks"):
  with members:
    - "places an image database for deep scene understanding"
    - "spice semantic propositional image caption evaluation"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual question answering a survey of methods and datasets"
    - "deep markov random field for image modeling"
    - "visual relationship detection with language priors"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-6 (induced by source paper "going deeper with semantics video activity interpretation using semantic contextualization"):
  with members:
    - "scene graph generation by iterative message passing"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "asynchronous temporal fields for action recognition"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "vqa visual question answering"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

Connected through hyperedge HE-7 (induced by source paper "spice semantic propositional image caption evaluation"):
  with members:
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "language models for image captioning the quirks and what works"
    - "robust subgraph generation improves abstract meaning representation parsing"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

Connected through hyperedge HE-8 (induced by source paper "re evaluating automatic metrics for image captioning"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "spice semantic propositional image caption evaluation"
    - "show attend and tell neural image caption generation with visual attention"
    - "guiding long short term memory for image caption generation"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "cider consensus based image description evaluation"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"

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)