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: "learning emoji embeddings using emoji co occurrence network graph"
  Abstract: Usage of emoji in social media platforms has seen a rapid increase over the last few years. Majority of the social media posts are laden with emoji and users often use more than one emoji in a single social media post to express their emotions and to emphasize certain words in a message. Utilizing the emoji co-occurrence can be helpful to understand how emoji are used in social media posts and their meanings in the context of social media posts. In this paper, we investigate whether emoji co-occurrences can be used as a feature to learn emoji embeddings which can be used in many downstream applications such sentiment analysis and emotion identification in social media text. We utilize 147 million tweets which have emojis in them and build an emoji co-occurrence network. Then, we train a network embedding model to embed emojis into a low dimensional vector space. We evaluate our embeddings using sentiment analysis and emoji similarity experiments, and experimental results show that our embeddings outperform the current state-of-the-art results for sentiment analysis tasks.

Connected through hyperedge HE-1 (induced by source paper "multimodal emotion classification"):
  with members:
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "which emoji talks best for my picture"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "which emoji talks best for my picture"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "advances in pre training distributed word representations"
    - "are emojis predictable"
    - "fasttext zip compressing text classification models"

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.SI(Social and Information Networks)