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 "mining and forecasting career trajectories of music artists"):
  Abstract: Many musicians, from up-and-comers to established artists, rely heavily on performing live to promote and disseminate their music. To advertise live shows, artists often use concert discovery platforms that make it easier for their fans to track tour dates. In this paper, we ask whether digital traces of live performances generated on those platforms can be used to understand career trajectories of artists. First, we present a new dataset we constructed by cross-referencing data from such platforms. We then demonstrate how this dataset can be used to mine and predict important career milestones for the musicians, such as signing by a major music label, or performing at a certain venue. Finally, we perform a temporal analysis of the bipartite artist-venue graph, and demonstrate that high centrality on this graph is correlated with success.

Includes members:
  Member "discovering hidden structure in high dimensional human behavioral data via tensor factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning behavioral representations from wearable sensors")
  Member "performance dynamics and success in online games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep neural networks for optimal team composition")
  Member "birank towards ranking on bipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "style in the age of instagram predicting success within the fashion industry using social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how algorithmic popularity bias hinders or promotes quality")
  Member "scalable temporal latent space inference for link prediction in dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning")
  Member "analysis of a heterogeneous social network of humans and cultural objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a general centrality framework based on node navigability")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")
  Member "online popularity and topical interests through the lens of instagram":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mining social media data for biomedical signals and health related behavior")

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)