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 "the power of dynamic social networks to predict individuals mental health"):
  Abstract: Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our knowledge, we are the first to do this. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without developing a predictive model; or they study other individual traits but not mental health. In a systematic and comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data.

Includes members:
  Member "heterogeneous network approach to predict individuals mental health":
    also connected through other hyperedges:
      (none)
  Member "social network structure is predictive of health and wellness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "heterogeneous network approach to predict individuals mental health")
  Member "daily stress recognition from mobile phone data weather conditions and individual traits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "heterogeneous network approach to predict individuals mental health")
  Member "exploring the structure and function of temporal networks with dynamic graphlets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "item independent temporal motifs to summarize and compare temporal networks")
  Member "dynamic networks reveal key players in aging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fleet butterfly estimation from a bipartite graph stream")
  Member "happiness is assortative in online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "friendship paradox biases perceptions in directed networks")

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)