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: "an army of me sockpuppets in online discussion communities"
  Abstract: In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as ``I'', and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.

Connected through hyperedge HE-1 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"

Connected through hyperedge HE-2 (induced by source paper "predicting the role of political trolls in social media"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "claimrank detecting check worthy claims in arabic and english"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"
    - "the effect of collective attention on controversial debates on social media"

Connected through hyperedge HE-3 (induced by source paper "analysing russian trolls via nlp tools"):
  with members:
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "partisanship propaganda and post truth politics quantifying impact in online debate"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "the spread of low credibility content by social bots"
    - "the spread of fake news by social bots"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-4 (induced by source paper "into the battlefield quantifying and modeling intra community conflicts in online discussion"):
  with members:
    - "how did the discussion go discourse act classification in social media conversations"
    - "multi task learning for target dependent sentiment classification"
    - "link prediction based on graph neural networks"
    - "community interaction and conflict on the web"
    - "a long term analysis of polarization on twitter"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "an ensemble method to produce high quality word embeddings"

Connected through hyperedge HE-5 (induced by source paper "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"):
  with members:
    - "who let the trolls out towards understanding state sponsored trolls"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "debatenight the role and influence of socialbots on twitter during the 1st u s presidential debate"
    - "the spread of fake news by social bots"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"):
  with members:
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "the spread of low credibility content by social bots"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "ensemfdet an ensemble approach to fraud detection based on bipartite graph"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "spam review detection with graph convolutional networks"
    - "antisocial behavior in online discussion communities"
    - "vews a wikipedia vandal early warning system"

Connected through hyperedge HE-8 (induced by source paper "sgp spotting groups polluting the online political discourse"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"

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)