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: "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
  Abstract: The friendship paradox is the phenomenon that in social networks, people on average have fewer friends than their friends do. The generalized friendship paradox is an extension to attributes other than the number of friends. The friendship paradox and its generalized version have gathered recent attention due to the information they provide about network structure and local inequalities. In this paper, we propose several measures of nodal qualities which capture different aspects of their activities and influence in online social networks. Using these measures we analyse the prevalence of the generalized friendship paradox over Twitter and we report high levels of prevalence (up to over 90% of nodes). We contend that this prevalence of the friendship paradox and its generalized version arise because of the hierarchical nature of the connections in the network. This hierarchy is nested as opposed to being star-like. We conclude that these paradoxes are collective phenomena not created merely by a minority of well-connected or high-attribute nodes. Moreover, our results show that a large fraction of individuals can experience the generalized friendship paradox even in the absence of a significant correlation between degrees and attributes.

Connected through hyperedge HE-1 (induced by source paper "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling"):
  with members:
    - "information diffusion in social networks friendship paradox based models and statistical inference"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "the majority illusion in social networks"
    - "tracking infection diffusion in social networks filtering algorithms and threshold bounds"
    - "network weirdness exploring the origins of network paradoxes"
    - "friendship paradox redux your friends are more interesting than you"
    - "using friends as sensors to detect global scale contagious outbreaks"

Connected through hyperedge HE-2 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox"):
  with members:
    - "information diffusion in social networks friendship paradox based models and statistical inference"
    - "friendship paradox biases perceptions in directed networks"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "gemsec graph embedding with self clustering"
    - "the majority illusion in social networks"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"

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)