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: "word embeddings to enhance twitter gang member profile identification"
  Abstract: Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members social media posts.

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 "examining uk drill music through sentiment trajectory analysis"):
  with members:
    - "identifying the sentiment styles of youtube s vloggers"
    - "the emotional arcs of stories are dominated by six basic shapes"

Connected through hyperedge HE-3 (induced by source paper "a semantics based measure of emoji similarity"):
  with members:
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"
    - "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-4 (induced by source paper "unsupervised spatio temporal embeddings for user and location modelling"):
  with members:
    - "a structured self attentive sentence embedding"
    - "semantic place descriptors for classification and map discovery"
    - "from word embeddings to item recommendation"
    - "gt seer geo temporal sequential embedding rank for point of interest recommendation"
    - "deep residual learning for image recognition"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "deepwalk online learning of social representations"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-5 (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"
    - "finding street gang members on twitter"
    - "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-6 (induced by source paper "emojinet building a machine readable sense inventory for emoji"):
  with members:
    - "finding street gang members on twitter"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

Connected through hyperedge HE-7 (induced by source paper "finding street gang members on twitter"):
  with members:
    - "user taglines alternative presentations of expertise and interest in social media"

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)