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 "multimodal emotion classification"):
  Abstract: Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98% is achieved with a training data of 550k posts.

Includes members:
  Member "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning")
  Member "which emoji talks best for my picture":
    also connected through other hyperedges:
      (none)
  Member "learning emoji embeddings using emoji co occurrence network graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "which emoji talks best for my picture")
  Member "multimodal emoji prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")
  Member "a semantics based measure of emoji similarity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "which emoji talks best for my picture")
  Member "fasttext zip compressing text classification models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "emojinet an open service and api for emoji sense discovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predictive analysis on twitter techniques and applications")
  Member "finding street gang members on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predictive analysis on twitter techniques and applications")

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)