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: "robust image sentiment analysis using progressively trained and domain transferred deep networks"
  Abstract: Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.

Connected through hyperedge HE-1 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey"):
  with members:
    - "multi source domain adaptation for text classification via distancenet bandits"
    - "multi source domain adaptation for visual sentiment classification"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "multi source domain adaptation for visual sentiment classification"):
  with members:
    - "multi source domain adaptation for semantic segmentation"
    - "moment matching for multi source domain adaptation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "correlation alignment for unsupervised domain adaptation"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"

Connected through hyperedge HE-3 (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-4 (induced by source paper "weakly supervised body part parsing with pose based part priors"):
  with members:
    - "graphonomy universal human parsing via graph transfer learning"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "synthesizing images of humans in unseen poses"
    - "instance level human parsing via part grouping network"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "human semantic parsing for person re identification"
    - "weakly and semi supervised panoptic segmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-5 (induced by source paper "deep sentiment sentiment analysis using ensemble of cnn and bi lstm models"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "deep learning for hate speech detection in tweets"
    - "automatic question answering using a deep similarity neural network"
    - "mask r cnn"
    - "deep reinforcement learning for dialogue generation"
    - "ssd single shot multibox detector"
    - "the darpa twitter bot challenge"

Connected through hyperedge HE-6 (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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "learning affective correspondence between music and image"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "audio visual scene analysis with self supervised multisensory features"
    - "content based video music retrieval using soft intra modal structure constraint"
    - "objects that sound"
    - "look listen and learn"
    - "ambient sound provides supervision for visual learning"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "learning multi level deep representations for image emotion classification"

Connected through hyperedge HE-8 (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"
    - "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"

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)