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 "like comment repin user interaction on pinterest"):
  Abstract: We present the results of a study of the Pinterest activity graph. Pinterest is a relatively new and extremely popular content-based social network. Building on a body of work showing that the hidden network whose edges are actual interactions between users is more informative about social relationships that the follower-following network, we study the activity graph composed of links formed by liking, commenting, and repinning, and show that it is very different from the follow network. We collect data about 14 million pins, 7 million repins, 1.6 million likes, and several hundred thousand users and report interesting results about social activity on Pinterest. In particular, we discover that only 12.3% of a user's followers interact with their pins, and over 70% of activity on each user's boards is done by non-followers, but on average, followers who are active perform twice as many actions as non-followers.

Includes members:
  Member "the pin bang theory discovering the pinterest world":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what is tumblr a statistical overview and comparison")
  Member "towards unbiased bfs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "feedback loops of attention in peer production":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "laboratories of oligarchy how the iron law extends to peer production")
  Member "social networks that matter twitter under the microscope":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consistent recovery threshold of hidden nearest neighbor graphs")

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)