human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neighbor neighbor correlations explain measurement bias in networks"):
  Abstract: In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.

Includes members:
  Member "the majority illusion in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "identification of core periphery structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "link prediction in networks with core fringe data")
  Member "network weirdness exploring the origins of network paradoxes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "friendship paradox biases perceptions in directed networks")
  Member "generalized friendship paradox in complex networks the case of scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "friendship paradox biases perceptions in directed networks")
  Member "friendship paradox redux your friends are more interesting than you":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "virality prediction and community structure in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "core periphery structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral density of equitable core periphery graphs")
  Member "systematic topology analysis and generation using degree correlations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recent advances in scalable network generation")

Question: Please tell me which class the source paper 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)