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: "aggregate characterization of user behavior in twitter and analysis of the retweet graph"
  Abstract: Most previous analysis of Twitter user behavior has focused on individual information cascades and the social followers graph, in which the nodes for two users are connected if one follows the other. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, that the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and that the inter-tweet interval distribution is a power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users than the social graph. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems---both predicting real-word performance and detecting spam---are discussed.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-2 (induced by source paper "multidimensional outlier detection in temporal interaction networks an application to political communication on twitter"):
  with members:
    - "characterizing and detecting hateful users on twitter"
    - "online human bot interactions detection estimation and characterization"
    - "multiscale event detection in social media"
    - "modelling of trends in twitter using retweet graph dynamics"

Connected through hyperedge HE-3 (induced by source paper "subgraph classification clustering and centrality for a degree asymmetric twitter based graph case study suicidality"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "sample nlpde and nlode social media modeling of information transmission for infectious diseases case study ebola"

Connected through hyperedge HE-4 (induced by source paper "twawler a lightweight twitter crawler"):
  with members:
    - "the darpa twitter bot challenge"
    - "understanding types of users on twitter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "there is something beyond the twitter network"):
  with members:
    - "ultrametricity of information cascades"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-6 (induced by source paper "evolving twitter an experimental analysis of graph properties of the social graph"):
  with members:
    - "two types of well followed users in the followership networks of twitter"
    - "rethinking centrality the role of dynamical processes in social network analysis"
    - "four degrees of separation"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy models of shortest path and outbreak distributions in networks"):
  with members:
    - "strong regularities in growth and decline of popularity of social media services"
    - "computing the kullback leibler divergence between two generalized gamma distributions"
    - "structure and dynamics of information pathways in online 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)