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: "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
  Abstract: This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (ANPs) automatically discovered from the tags of web photos, and can be utilized as effective statistical cues for detecting emotions depicted in the images. Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts. We adopt the popular model of deep convolutional neural networks which recently shows great performance improvement on classifying large-scale web-based image dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a newly developed deep learning framework. To deal with the biased training data which only contains images with strong sentiment and to prevent overfitting, we initialize the model with the model weights trained from ImageNet. Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.

Connected through hyperedge HE-1 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-2 (induced by source paper "pdanet polarity consistent deep attention network for fine grained visual emotion regression"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "cbam convolutional block attention module"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "squeeze and excitation networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-3 (induced by source paper "natural disasters detection in social media and satellite imagery a survey"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "disaster monitoring using unmanned aerial vehicles and deep learning"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "deep cross residual learning for multitask visual recognition"
    - "enriching word vectors with subword information"
    - "large scale image retrieval with attentive deep local features"
    - "nazr cnn object detection and fine grained classification in crowdsourced uav images"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-4 (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"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"

Connected through hyperedge HE-5 (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"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "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"

Connected through hyperedge HE-6 (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"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "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"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "towards emotion retrieval in egocentric photostream"):
  with members:
    - "sentiment recognition in egocentric photostreams"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "going deeper into first person activity recognition"
    - "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"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"

Connected through hyperedge HE-8 (induced by source paper "an overview of computational approaches for analyzing interpretation"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "multimodal social media analysis for gang violence prevention"
    - "adversarial defense based on structure to signal autoencoders"
    - "what do deep networks like to see"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"

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)