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: "predicting successful memes using network and community structure"
  Abstract: We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

Connected through hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts"):
  with members:
    - "privacy aware recommendation with private attribute protection using adversarial learning"
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "detection of violent extremists in social media"
    - "detecting pathogenic social media accounts without content or network structure"
    - "similar but different exploiting users congruity for recommendation systems"
    - "predicting factuality of reporting and bias of news media sources"

Connected through hyperedge HE-2 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (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-4 (induced by source paper "early identification of pathogenic social media accounts"):
  with members:
    - "detecting pathogenic social media accounts without content or network structure"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "securing social media user data an adversarial approach"
    - "deep learning for hate speech detection in tweets"
    - "semi supervised learning for detecting human trafficking"
    - "the spread of fake news by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-5 (induced by source paper "memesequencer sparse matching for embedding image macros"):
  with members:
    - "modeling image virality with pairwise spatial transformer networks"
    - "information evolution in social networks"
    - "do cascades recur"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "end to end localization and ranking for relative attributes"
    - "tensorflow a system for large scale machine learning"
    - "understanding image virality"
    - "skip thought vectors"

Connected through hyperedge HE-6 (induced by source paper "neural diffusion model for microscopic cascade prediction"):
  with members:
    - "attention is all you need"
    - "who will share my image predicting the content diffusion path in online social networks"
    - "topological recurrent neural network for diffusion prediction"
    - "semi supervised classification with graph convolutional networks"
    - "deepcas an end to end predictor of information cascades"
    - "deep residual learning for image recognition"
    - "the simple rules of social contagion"
    - "line large scale information network embedding"

Connected through hyperedge HE-7 (induced by source paper "a tensor based data model for polystore an application to social networks data"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "polystore mathematics of relational algebra"
    - "the bigdawg polystore system and architecture"
    - "measuring user influence on twitter"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "the sql query language configurable unifying and semi structured"
    - "exposing multi relational networks to single relational network analysis algorithms"

Connected through hyperedge HE-8 (induced by source paper "on the origins of memes by means of fringe web communities"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "memesequencer sparse matching for embedding image macros"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "on the efficiency of the information networks in social media"
    - "information evolution in social networks"
    - "tensorflow a system for large scale machine learning"
    - "clustering memes in social media"
    - "truthy mapping the spread of astroturf in microblog streams"

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)