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: "visual sentiment prediction with deep convolutional neural networks"
  Abstract: Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.

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 "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"):
  with members:
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "transductive multi view zero shot learning"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "multi view metric learning for multi view video summarization"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (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"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "from pixels to sentiment fine tuning cnns for visual sentiment prediction"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "cultural event recognition with visual convnets and temporal models"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "analyzing the performance of multilayer neural networks for object recognition"

Connected through hyperedge HE-5 (induced by source paper "visual themes and sentiment on social networks to aid first responders during crisis events"):
  with members:
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-6 (induced by source paper "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"):
  with members:
    - "cultural event recognition with visual convnets and temporal models"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "object detectors emerge in deep scene cnns"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (induced by source paper "mapping images to sentiment adjective noun pairs with factorized neural nets"):
  with members:
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"

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)