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: "spice up your chat the intentions and sentiment effects of using emoji"
  Abstract: Emojis, as a new way of conveying nonverbal cues, are widely adopted in computer-mediated communications. In this paper, first from a message sender perspective, we focus on people's motives in using four types of emojis -- positive, neutral, negative, and non-facial. We compare the willingness levels of using these emoji types for seven typical intentions that people usually apply nonverbal cues for in communication. The results of extensive statistical hypothesis tests not only report the popularities of the intentions, but also uncover the subtle differences between emoji types in terms of intended uses. Second, from a perspective of message recipients, we further study the sentiment effects of emojis, as well as their duplications, on verbal messages. Different from previous studies in emoji sentiment, we study the sentiments of emojis and their contexts as a whole. The experiment results indicate that the powers of conveying sentiment are different between four emoji types, and the sentiment effects of emojis vary in the contexts of different valences.

Connected through hyperedge HE-1 (induced by source paper "sentimoji an emoji powered learning approach for sentiment analysis in software engineering"):
  with members:
    - "a benchmark study on sentiment analysis for software engineering research"
    - "on the use of emoticons in open source software development"
    - "sentiment polarity detection for software development"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "a first look at emoji usage on github an empirical study"
    - "attention is all you need"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "recommending insightful comments for source code using crowdsourced knowledge"

Connected through hyperedge HE-2 (induced by source paper "i stand with you using emojis to study solidarity in crisis events"):
  with members:
    - "emojinet building a machine readable sense inventory for emoji"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

Connected through hyperedge HE-3 (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"
    - "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"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-4 (induced by source paper "ermes emoji powered representation learning for cross lingual sentiment classification"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "tensorflow a system for large scale machine learning"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"):
  with members:
    - "mining the relationship between emoji usage patterns and personality"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "sense embedding learning for word sense induction"
    - "emoji2vec learning emoji representations from their description"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "through a gender lens learning usage patterns of emojis from large scale android users"):
  with members:
    - "emoji2vec learning emoji representations from their description"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "line large scale information network embedding"
    - "sentiment of emojis"
    - "privacy tradeoffs in predictive analytics"

Connected through hyperedge HE-8 (induced by source paper "a first look at emoji usage on github an empirical study"):
  with members:
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "modelling politics in requirements engineering adding emoji to existing notations"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"
    - "the role of emotions in contributors activity a case study on the gentoo community"

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)