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 a large scale dataset for image emotion recognition the fine print and the benchmark"
  Abstract: Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.

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"
    - "coupled generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "a comprehensive survey of deep learning for image captioning"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (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-5 (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-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"
    - "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-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"
    - "learning multi level deep representations for image emotion classification"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"

Connected through hyperedge HE-8 (induced by source paper "human centered emotion recognition in animated gifs"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "temporal segment networks towards good practices for deep action recognition"
    - "learning multi level deep representations for image emotion classification"
    - "video2gif automatic generation of animated gifs from video"
    - "hierarchical multiscale recurrent neural networks"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deep residual learning for image recognition"
    - "deep learning using linear support vector machines"

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.AI(Artificial Intelligence)