human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "smile be happy emoji embedding for visual sentiment analysis"):
  Abstract: Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNet. However, objects are sentiment neutral which hinders the expected gain of transfer learning for such tasks. In this work, we propose to overcome this problem by learning a novel sentiment-aligned image embedding that is better suited for subsequent visual sentiment analysis. Our embedding leverages the intricate relation between emojis and images in large-scale and readily available data from social media. Emojis are language-agnostic, consistent, and carry a clear sentiment signal which make them an excellent proxy to learn a sentiment aligned embedding. Hence, we construct a novel dataset of $4$ million images collected from Twitter with their associated emojis. We train a deep neural model for image embedding using emoji prediction task as a proxy. Our evaluation demonstrates that the proposed embedding outperforms the popular object-based counterpart consistently across several sentiment analysis benchmarks. Furthermore, without bell and whistles, our compact, effective and simple embedding outperforms the more elaborate and customized state-of-the-art deep models on these public benchmarks. Additionally, we introduce a novel emoji representation based on their visual emotional response which support a deeper understanding of the emoji modality and their usage on social media.

Includes members:
  Member "i stand with you using emojis to study solidarity in crisis events":
    also connected through other hyperedges:
      (none)
  Member "the new modality emoji challenges in prediction anticipation and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predict emoji combination with retrieval strategy")
  Member "building emotional machines recognizing image emotions through deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deriving emotions and sentiments from visual content a disaster analysis use case")
  Member "recovering the missing link predicting class attribute associations for unsupervised zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "any shot object detection")
  Member "emoji2vec learning emoji representations from their description":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "building a large scale dataset for image emotion recognition the fine print and the benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey")
  Member "from pixels to sentiment fine tuning cnns for visual sentiment prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "outdoorsent sentiment analysis of urban outdoor images by using semantic and deep features")
  Member "robust image sentiment analysis using progressively trained and domain transferred deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey")

Question: Please tell me which class the source paper 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)