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 "multiscale mixing patterns in networks"):
  Abstract: Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks, manifesting as a higher tendency of links occurring between people of the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the organization of complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of categorical metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localize this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently, we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that, for many real-world networks, the distribution of assortativity is skewed, overdispersed, and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.

Includes members:
  Member "bias and variance in the social structure of gender":
    also connected through other hyperedges:
      (none)
  Member "the many facets of community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "embedding based silhouette community detection")
  Member "block models and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diffusion improves graph learning")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "the majority illusion in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "graph based semi supervised learning for relational networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "generalized friendship paradox in complex networks the case of scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "friendship paradox biases perceptions in directed networks")
  Member "social structure of facebook networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)