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: "emoji2vec learning emoji representations from their description"
  Abstract: Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation.

Connected through hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "measuring similarity between brands using followers post in social media"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "what your facebook profile picture reveals about your personality"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "tag prediction at flickr a view from the darkroom"
    - "item2vec neural item embedding for collaborative filtering"
    - "enriching word vectors with subword information"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "time to take emoji seriously they vastly improve casual conversational models"):
  with members:
    - "a repository of conversational datasets"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "training millions of personalized dialogue agents"
    - "a large scale corpus for conversation disentanglement"
    - "coqa a conversational question answering challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "self representation on twitter using emoji skin color modifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "smile be happy emoji embedding for visual sentiment analysis"):
  with members:
    - "i stand with you using emojis to study solidarity in crisis events"
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "caire_hkust at semeval 2019 task 3 hierarchical attention for dialogue emotion classification"):
  with members:
    - "universal transformers"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "deep contextualized word representations"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification"):
  with members:
    - "deep contextualized word representations"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "focal loss for dense object detection"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "distance based self attention network for natural language inference"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "studying cultural differences in emoji usage across the east and the west"):
  with members:
    - "what twitter profile and posted images reveal about depression and anxiety"
    - "understanding and measuring psychological stress using social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "nelec at semeval 2019 task 3 think twice before going deep"):
  with members:
    - "cyclical learning rates for training neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "thumbs up sentiment classification using machine learning techniques"

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)