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 "real time enhanced random sampling of online social networks"):
  Abstract: Social graphs can be easily extracted from Online Social Networks. However these networks are getting larger from day to day. Sampling methods used to evaluate graph information cannot accurately extract graph properties. Furthermore Social Networks are limiting the access to their data, making the crawling process even harder. A novel approach on Random Sampling is proposed, considering both limitation and resources. We evaluate this proposal with 4 different settings on 5 different Test Graphs, crawled directly from Twitter. Through comparing the results we observe the pros and cons of its method as well as their resource allocation. Concluding we present their best area of application.

Includes members:
  Member "graph size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "multigraph sampling of online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling online social networks by random walk with indirect jumps")
  Member "estimating and sampling graphs with multidimensional random walks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")
  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)