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: "multimodal emoji prediction"
  Abstract: Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.

Connected through hyperedge HE-1 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "understanding chat messages for sticker recommendation in hike messenger"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "product aware answer generation in e commerce question answering"
    - "how to write summaries with patterns learning towards abstractive summarization through prototype editing"
    - "abstractive text summarization by incorporating reader comments"
    - "think visually question answering through virtual imagery"
    - "transfer learning via unsupervised task discovery for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "understanding chat messages for sticker recommendation in hike messenger"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "efficient natural language response suggestion for smart reply"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-3 (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"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "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-4 (induced by source paper "understanding chat messages for sticker recommendation in messaging apps"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "smart reply automated response suggestion for email"
    - "character level convolutional networks for text classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "predict emoji combination with retrieval strategy"):
  with members:
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "are emojis predictable"

Connected through hyperedge HE-6 (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"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "a semantics based measure of emoji similarity"
    - "advances in pre training distributed word representations"
    - "are emojis predictable"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-7 (induced by source paper "exploring emoji usage and prediction through a temporal variation lens"):
  with members:
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "a semantics based measure of emoji similarity"
    - "are emojis predictable"
    - "emojinet an open service and api for emoji sense discovery"
    - "emoji2vec learning emoji representations from their description"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)