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: "finding street gang members on twitter"
  Abstract: Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-2 (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"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "detecting gang involved escalation on social media using context"):
  with members:
    - "identifying civilians killed by police with distantly supervised entity event extraction"
    - "demographic dialectal variation in social media a case study of african american english"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "a semantics based measure of emoji similarity"):
  with members:
    - "emojinet an open service and api for emoji sense discovery"
    - "word embeddings to enhance twitter gang member profile identification"
    - "emojinet building a machine readable sense inventory for emoji"
    - "emoji2vec learning emoji representations from their description"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "sentiment of emojis"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "emojinet an open service and api for emoji sense discovery"):
  with members:
    - "emojinet building a machine readable sense inventory for emoji"
    - "emoji2vec learning emoji representations from their description"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "tracking illicit drug dealing and abuse on instagram using multimodal analysis"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "america tweets china a fine grained analysis of the state and individual characteristics regarding attitudes towards china"
    - "pinterest board recommendation for twitter users"
    - "social media image analysis for public health"
    - "monitoring potential drug interactions and reactions via network analysis of instagram user timelines"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "knowledge will propel machine understanding of content extrapolating from current examples"):
  with members:
    - "a semantics based measure of emoji similarity"
    - "emojinet an open service and api for emoji sense discovery"
    - "word embeddings to enhance twitter gang member profile identification"
    - "emojinet building a machine readable sense inventory for emoji"
    - "implicit entity linking in tweets"
    - "a hybrid approach to finding relevant social media content for complex domain specific information needs"

Connected through hyperedge HE-8 (induced by source paper "emojinet building a machine readable sense inventory for emoji"):
  with members:
    - "word embeddings to enhance twitter gang member profile identification"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

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.SI(Social and Information Networks)