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 "what makes social search efficient"):
  Abstract: The idea of the small world first put forth by Milgram in the 1960's shows empirically how people knowing reliably only connections to their direct contacts can leverage their knowledge to perform an efficient global search, referred to as social search, in surprisingly few steps. Later, it was established that social networks are often interconnected in such a way that knowledge of all the edges enables a search in even a smaller number of step; such networks are often called small-world networks. Yet, despite a diverse body of work on the social search and its efficiency, it has been unclear why nodes with limited knowledge of just direct links are able to route efficiently. To probe this question, here we use a real location-based social network, Gowalla, to emulate a synthetic social search task. The results demonstrate that the spatial distributions of friends, and friends of friends (FoF) as well as the types of information utilized for search play a key role in effective social search. We also establish that neither the ways nodes are embedded into space nor edges distributed among nodes are important for social search efficiency. Moreover, we show that even very limited knowledge of friends of friends significantly improves social search performance with gains growing most rapidly for small fractions of FoF knowledge.

Includes members:
  Member "probing limits of information spread with sequential seeding":
    also connected through other hyperedges:
      (none)
  Member "using location based social networks to validate human mobility and relationships models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tweeting over the border an empirical study of transnational migration in san diego and tijuana")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diffusion improves graph learning")

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)