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 pixels to sentiment fine tuning cnns for visual sentiment prediction"
  Abstract: Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.

Connected through hyperedge HE-1 (induced by source paper "smile be happy emoji embedding for visual sentiment analysis"):
  with members:
    - "i stand with you using emojis to study solidarity in crisis events"
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "emoji2vec learning emoji representations from their description"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "outdoorsent sentiment analysis of urban outdoor images by using semantic and deep features"):
  with members:
    - "gender matters analyzing global cultural gender preferences for venues using social sensing"
    - "yolo9000 better faster stronger"
    - "yfcc100m the new data in multimedia research"
    - "smelly maps the digital life of urban smellscapes"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-3 (induced by source paper "e sports talent scouting based on multimodal twitch stream data"):
  with members:
    - "twitch plays pokemon machine learns twitch unsupervised context aware anomaly detection for identifying trolls in streaming data"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "in defense of the triplet loss for person re identification"
    - "video highlight prediction using audience chat reactions"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "soundnet learning sound representations from unlabeled video"
    - "gendered conversation in a social game streaming platform"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "building emotional machines recognizing image emotions through deep neural networks"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "more cat than cute interpretable prediction of adjective noun pairs"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "going deeper for multilingual visual sentiment detection"
    - "deep cross residual learning for multitask visual recognition"
    - "densely connected convolutional networks"
    - "mapping images to sentiment adjective noun pairs with factorized neural nets"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"

Connected through hyperedge HE-6 (induced by source paper "when saliency meets sentiment understanding how image content invokes emotion and sentiment"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "object detectors emerge in deep scene cnns"
    - "caffe convolutional architecture for fast feature embedding"

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)