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: "are emojis predictable"
  Abstract: Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. In this paper, we investigate the relation between words and emojis, studying the novel task of predicting which emojis are evoked by text-based tweet messages. We train several models based on Long Short-Term Memory networks (LSTMs) in this task. Our experimental results show that our neural model outperforms two baselines as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.

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"
    - "multimodal emoji prediction"

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"
    - "multimodal emoji prediction"
    - "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"

Connected through hyperedge HE-3 (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"
    - "multimodal emoji prediction"
    - "attention is all you need"

Connected through hyperedge HE-4 (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"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "advances in pre training distributed word representations"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-5 (induced by source paper "multimodal emoji prediction"):
  with members:
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "exploring emoji usage and prediction through a temporal variation lens"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "a semantics based measure of emoji similarity"
    - "emojinet an open service and api for emoji sense discovery"
    - "emoji2vec learning emoji representations from their description"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-6 (induced by source paper "cross lingual task specific representation learning for text classification in resource poor languages"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "transition based dependency parsing with stack long short term memory"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "distributed representations of words and phrases and their compositionality"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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"
    - "multimodal emoji prediction"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "a semantics based measure of emoji similarity"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "contrastive learning of emoji based representations for resource poor languages"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

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)