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: "building emotional machines recognizing image emotions through deep neural networks"
  Abstract: An image is a very effective tool for conveying emotions. Many researchers have investigated in computing the image emotions by using various features extracted from images. In this paper, we focus on two high level features, the object and the background, and assume that the semantic information of images is a good cue for predicting emotion. An object is one of the most important elements that define an image, and we find out through experiments that there is a high correlation between the object and the emotion in images. Even with the same object, there may be slight difference in emotion due to different backgrounds, and we use the semantic information of the background to improve the prediction performance. By combining the different levels of features, we build an emotion based feed forward deep neural network which produces the emotion values of a given image. The output emotion values in our framework are continuous values in the 2-dimensional space (Valence and Arousal), which are more effective than using a few number of emotion categories in describing emotions. Experiments confirm the effectiveness of our network in predicting the emotion of images.

Connected through hyperedge HE-1 (induced by source paper "deriving emotions and sentiments from visual content a disaster analysis use case"):
  with members:
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "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"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "generating persuasive visual storylines for promotional videos"):
  with members:
    - "learning video story composition via recurrent neural network"
    - "nima neural image assessment"
    - "building ethics into artificial intelligence"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning visual storylines with skipping recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "will people like your image learning the aesthetic space"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis from images of natural disasters"):
  with members:
    - "automatic detection of passable roads after floods in remote sensed and social media data"
    - "exploiting convnet diversity for flooding identification"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "contemplating visual emotions understanding and overcoming dataset bias"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "webvision database visual learning and understanding from web data"
    - "collaborative summarization of topic related videos"
    - "automatic understanding of image and video advertisements"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"

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)