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: "breakingnews article annotation by image and text processing"
  Abstract: Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of computer vision and natural language processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of News articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce new deep learning methods that address source detection, popularity prediction, article illustration and geolocation of articles. An adaptive CNN architecture is proposed, that shares most of the structure for all the tasks, and is suitable for multitask and transfer learning. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and popularity metrics). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.

Connected through hyperedge HE-1 (induced by source paper "good news everyone context driven entity aware captioning for news images"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "entity aware image caption generation"
    - "learning to evaluate image captioning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "rich image captioning in the wild"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping disjoint datasets for multilingual multimodal representation learning"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "lessons learned in multilingual grounded language learning"
    - "zero resource neural machine translation with multi agent communication game"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "limitations of cross lingual learning from image search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "emergent translation in multi agent communication"

Connected through hyperedge HE-3 (induced by source paper "semantic relatedness based re ranker for text spotting"):
  with members:
    - "universal sentence encoder"
    - "visual re ranking with natural language understanding for text spotting"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "visual attention models for scene text recognition"
    - "multichannel variable size convolution for sentence classification"
    - "a decomposable attention model for natural language inference"
    - "bag of tricks for efficient text classification"
    - "coco text dataset and benchmark for text detection and recognition in natural images"

Connected through hyperedge HE-4 (induced by source paper "predicting economic development using geolocated wikipedia articles"):
  with members:
    - "learning to interpret satellite images in global scale using wikipedia"
    - "learning to interpret satellite images using wikipedia"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "learning two branch neural networks for image text matching tasks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "text matching as image recognition"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-5 (induced by source paper "multimodal representation models for prediction and control from partial information"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "multimodal generative models for scalable weakly supervised learning"
    - "robots that can adapt like animals"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "shallow reading with deep learning predicting popularity of online content using only its title"):
  with members:
    - "predicting popularity of online videos using support vector regression"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "text understanding from scratch"
    - "a c lstm neural network for text classification"
    - "adam a method for stochastic optimization"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "the pulse of news in social media forecasting popularity"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "recurrent neural networks for online video popularity prediction"):
  with members:
    - "deep residual learning for image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "the pulse of news in social media forecasting popularity"
    - "predicting the popularity of online content"

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)