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 "centrality friendship paradoxes when our friends are more important than us"):
  Abstract: The friendship paradox states that, on average, our friends have more friends than we do. In network terms, the average degree over the nodes can never exceed the average degree over the neighbours of nodes. This effect, which is a classic example of sampling bias, has attracted much attention in the social science and network science literature, with variations and extensions of the paradox being defined, tested and interpreted. Here, we show that a version of the paradox holds rigorously for eigenvector centrality: on average, our friends are more important than us. We then consider general matrix-function centrality, including Katz centrality, and give sufficient conditions for the paradox to hold. We also discuss which results can be generalized to the cases of directed and weighted edges. In this way, we add theoretical support for a field that has largely been evolving through empirical testing.

Includes members:
  Member "the happiness paradox your friends are happier than you":
    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 "network weirdness exploring the origins of network paradoxes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "total communicability as a centrality measure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlocal pagerank")
  Member "using friends as sensors to detect global scale contagious outbreaks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")

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.DM(Discrete Mathematics)