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: "through a gender lens learning usage patterns of emojis from large scale android users"
  Abstract: Based on a large dataset of emoji usage collected from smartphone users across the world, this paper investigates usage of emojis from the gender perspective. We present various interesting findings that evidence a considerable difference in emoji usage between male and female users. Such a difference is significant not just in a statistical sense; it is sufficient for a machine learning algorithm to accurately infer the gender of a user purely based on the emojis used in their messages. In real-world scenarios where gender inference is a necessity, models based on emojis have unique advantages over existing models that are based on the textual or contextual information. Emojis not only provide the language-independent indicator, but also alleviate the risk of leaking private user information through the analysis of text and context.

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"
    - "a first look at emoji usage on github an empirical study"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "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 "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"
    - "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"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-3 (induced by source paper "ermes emoji powered representation learning for cross lingual sentiment classification"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "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-4 (induced by source paper "the new modality emoji challenges in prediction anticipation and retrieval"):
  with members:
    - "a semantics based measure of emoji similarity"
    - "sentiment perception of readers and writers in emoji use"
    - "emojinet building a machine readable sense inventory for emoji"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "yfcc100m the new data in multimedia research"
    - "the new data and new challenges in multimedia research"
    - "going deeper with convolutions"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-5 (induced by source paper "a first look at emoji usage on github an empirical study"):
  with members:
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "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.HC(Human-Computer Interaction)